Metadata-Version: 2.4
Name: shardloom
Version: 0.2.1
Summary: Pre-release Python client for ShardLoom, a Vortex-first no-fallback evidence-certified local compute engine
Author: ShardLoom contributors
License-Expression: Apache-2.0
Project-URL: Homepage, https://shardloom.io
Project-URL: Repository, https://github.com/depsilon/shardloom
Project-URL: Issues, https://github.com/depsilon/shardloom/issues
Project-URL: Documentation, https://github.com/depsilon/shardloom/tree/main/docs
Keywords: analytics,columnar,vortex,data-engine,etl,benchmark,no-fallback,rust,python
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Provides-Extra: materialization
Requires-Dist: numpy; extra == "materialization"
Requires-Dist: pandas; extra == "materialization"
Requires-Dist: pyarrow; extra == "materialization"
Dynamic: license-file

# ShardLoom Python CLI Client

This package is the first thin Python surface for ShardLoom, a Vortex-native,
no-fallback, evidence-certified local compute engine. It invokes the workspace
`shardloom` CLI with `--format json`, parses the stable `OutputEnvelope`, and
preserves typed result/artifact/certificate payloads, diagnostics, fallback
status, and the temporary legacy field mirror.

It is intentionally not a native binding, broad DataFrame API, broad SQL runtime, UDF runtime, or
fallback execution path. Importing the package has no ShardLoom side effects. Work happens only when
a caller explicitly invokes a CLI command through `ShardLoomClient` or one of the scoped Python
helpers that wraps an evidence-backed CLI smoke.

Public status is owned by `docs/release/public-status-matrix.md`. This README may describe scoped
local Python surfaces, the current source version, and the approved package track, but it does not
authorize production support, performance claims, Spark displacement, or hidden external execution.

## Local Use

From the repository root:

```powershell
$env:PYTHONPATH = "python\src"
python -c "from shardloom import ShardLoomClient; print(ShardLoomClient.from_repo().status().status)"
```

Or install the source-tree package in editable mode for notebook, job, or
Foundry-style imports:

```powershell
python -m pip install -e python
```

The source package exposes the current technical-preview source version through
`shardloom.__version__`. Public package channels can lag the source tree until the release contract
is advanced, so source checkouts should use editable installs and published-channel users should
install the latest released package from the selected channel. These channels are install access
only and do not imply production readiness, broad runtime support, or performance claims.

```sh
python -m pip install shardloom
```

Published supported-platform wheels resolve the packaged CLI before falling back to `PATH`.
Explicit binary/env/source configuration still wins. Use `SHARDLOOM_BIN` only when you want to pin a
specific CLI binary or when installing from a source distribution without a bundled platform CLI:

```powershell
$env:SHARDLOOM_BIN = "target\release\shardloom.exe"
```

Or pass an explicit binary:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient(binary="target/release/shardloom")
print(client.status().status)
```

`ShardLoomClient.from_repo()` looks for `target/release/shardloom` and then
`target/debug/shardloom` when a command is invoked. It does not run commands or
probe the repository at import time.

`ShardLoomClient.from_env()` is the import-friendly constructor for managed
Python environments. It reads configuration only and does not run commands:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_env()
smoke = client.smoke_check()
print(smoke.commands)
print(smoke.deployment_capabilities.field("surface_components"))
print(smoke.fallback_attempted)
```

Supported environment variables:

- `SHARDLOOM_BIN`: explicit `shardloom` CLI binary path.
- `SHARDLOOM_REPO_ROOT`: source checkout containing `target/<profile>/shardloom`.
- `SHARDLOOM_PROFILE_ORDER`: comma-separated target profile order, for example `release,debug`.
- `SHARDLOOM_TIMEOUT_SECONDS`: per-command subprocess timeout.

If no CLI binary is available, explicit client commands raise
`ShardLoomBinaryNotFoundError` with installation/configuration guidance instead
of leaking a raw subprocess error. The exception carries deterministic
no-fallback diagnostics plus a `shardloom.output.v2`-shaped error payload via
`to_error_payload(command)` for agents and wrappers that need protocol-shaped
missing-binary evidence. Importing the package and constructing
`ShardLoomClient.from_env()` remain side-effect-free.

For the CG-21 user workflow surface, use `shardloom.context()` when you want a
short import-friendly entry point for smoke checks and capability discovery:

```python
import shardloom as sl

ctx = sl.context()
smoke = ctx.smoke_check()
capabilities = ctx.capabilities()

print(smoke.python_package_version)
print(smoke.resolved_cli_path)
print(smoke.protocol_version)
print(smoke.fallback_attempted)
print(capabilities.python.field("scope"))
print(capabilities.sql_support.capability_state)
print(capabilities.fallback_attempted)
```

Constructing the context does not run ShardLoom, inspect datasets, probe object
stores, touch catalogs, execute SQL, or invoke external engines. The explicit
`smoke_check()` and `capabilities()` methods run only no-dataset CLI JSON
commands and preserve no-fallback status.

Capability views also expose a normalized posture object so Python callers can
inspect support, claim, runtime, effect, and policy state without scraping raw
CLI text:

```python
posture = capabilities.sql_support.posture

print(posture.support_status)
print(posture.claim_gate_status)
print(posture.report_only, posture.unsupported, posture.claim_grade)
print(posture.runtime_execution)
print(posture.data_read, posture.write_io, posture.object_store_io)
print(posture.fallback_attempted, posture.external_engine_invoked)
print(posture.required_evidence)
```

The posture view does not widen runtime support. It is a typed convenience
surface over existing `OutputEnvelope` fields and diagnostics. Unsupported or
report-only scopes remain unsupported or report-only, and
`fallback_attempted=false` / `external_engine_invoked=false` stay visible.

Use `ctx.user_surface_graduation_matrix()` to decide whether a Python or CLI
surface belongs on the ergonomic context path. The matrix uses five postures:
`high_level_context`, `client_only`, `diagnostic_only`, `feature_gated`, and
`not_user_facing`. `high_level_context` rows are the scoped workflows promoted
for normal context use; `client_only` rows stay explicit lower-level CLI/client
access; `diagnostic_only` and `feature_gated` rows must not be described as
runtime support without the matching evidence.

For normal Python use, start from the simple context and query surface. `repo_root` and
`profile_order` are optional development configuration overrides, not arguments users should have
to put in ordinary application code. Source-tree or CI runs can set `SHARDLOOM_BIN` or
`SHARDLOOM_REPO_ROOT` in the environment when the CLI is not on `PATH`.

`ctx.read(path)` is the normal public read wrapper. It infers `.csv`, `.json`, `.jsonl`, `.ndjson`,
`.parquet`, `.arrow`, `.ipc`, `.feather`, `.avro`, `.orc`, and `.vortex` local source adapters from
the path extension. Explicit helpers such as `read_csv(...)`, `read_json(...)`,
`read_parquet(...)`, `read_arrow_ipc(...)`, `read_avro(...)`, and `read_orc(...)` remain available
for compatibility, tests, and schema-pinned examples. ShardLoom owns SourceState, preparation,
execution, OutputPlan, replay, reuse, certificate, and no-fallback evidence behind the query
surface:

```python
import shardloom as sl

ctx = sl.context()
result = (
    ctx.read("target/orders.csv")
    .filter(sl.col("amount") >= 10)
    .select("id", "amount")
    .limit(100)
    .collect()
)

print(result.output_row_count)
print(result.first_result_row)
print(result.activation_summary.native_vortex_status)
print(result.activation_summary.execution_mode, result.activation_summary.applied_parallelism)
print(result.prepared_vortex_path)
print(result.vortex_ingest_performed)
print(result.claim_summary.claim_gate_status)
print(result.fallback_attempted, result.external_engine_invoked)
```

Every normal execution result exposes `activation_summary`, a compact view of route ID/status,
execution mode, native Vortex activation, required feature gate if any, parallelism,
pushdown/source-state signals when available, decode/materialization status, sink status,
fallback/external-engine flags, and claim-gate posture. Agents and notebooks should prefer this
summary before inspecting the full evidence envelope.

The same query shape can read admitted local formats through `ctx.read(...)` or the explicit
format helpers. CSV, flat JSON/JSONL/NDJSON, generated rows, and scoped local Vortex inputs are
the default public examples. Parquet, Arrow IPC/Feather, Avro, and ORC are admitted scoped
local-format surfaces when the matching feature-gated build is present; builds without those
readers return deterministic adapter blockers instead of invoking another engine. Compatibility
exports such as `write_jsonl(...)`, `write_csv(...)`, Parquet/Arrow IPC/Avro/ORC writers, fanout,
and quarantine sinks are admitted only when the workflow first carries Vortex preparation or native
Vortex-input evidence and the sink emits replay evidence such as `result_replay_verified`.
Format-specific behavior belongs at read/ingest and write/sink boundaries only; compute semantics
should lower through the shared ShardLoom/Vortex runtime or return a deterministic unsupported
report.
Agents and automation should use `docs/reference/shardloom-user-surface-index.md` and
`docs/reference/shardloom-user-surface-index.json` as the canonical map of Python, SQL, CLI,
generated-source, materialization, and deterministic blocker surfaces.
The canonical local output/sink scope is `docs/architecture/v1-local-output-sink-scope.md`; inspect
it with `ctx.local_output_sink_scope_report()` before treating a write helper as broader than its
scoped local evidence.

Bounded materialization is explicit. Local-source workflows can carry a `limit(...)` or pass
`collect(limit=...)`; SQL workflows can also pass `collect(limit=...)` or chain
`.limit(...).collect()`. Those admitted routes return typed report rows from the ShardLoom CLI
envelope. Decoded Python-object, pandas, Arrow, NumPy, and notebook materialization helpers are
bounded container/output boundaries over the admitted ShardLoom result; optional packages are never
used as execution engines and missing packages return deterministic diagnostics:

```python
preview_report = (
    ctx.read("target/orders.csv")
    .select("id", "amount")
    .limit(20)
    .collect()
)
print(preview_report.result_rows)

rows = ctx.read("target/orders.csv").select("id").limit(20).to_python_objects()
print(rows)

pandas_view = ctx.read("target/orders.csv").select("id").limit(20).to_pandas(check=False)
print(getattr(pandas_view, "blocker_id", None))
```

For workflows that need caller-scoped reuse evidence, `ctx.session(...)` and `sl.session(...)` expose
the same local read/SQL shapes as session-bound workflows:

```python
with ctx.session(session_id="orders-run") as sess:
    result = (
        sess.read_csv("target/orders.csv")
        .select("id", "amount")
        .limit(100)
        .collect()
    )
    repeat = sess.sql("SELECT id FROM 'target/orders.csv' LIMIT 100").collect()
    print(result.reuse_hit, repeat.source_state_reuse_hit)
```

The session is explicit, in-process, caller-owned, and closeable. It can reuse admitted local
`vortex_ingest` prepared-state reports plus admitted local collect reports when source and prepared
artifact fingerprints still match. Compatibility writes/fanout still require their native Vortex
export contracts before they are product routes. `ctx.prepare_vortex(...)`,
`ShardLoomClient.vortex_ingest_smoke(...)`, and raw runtime-envelope inspection remain lower-level
diagnostic surfaces. Session reuse is not a daemon, remote server, hidden global cache,
object-store/table cache, broad DataFrame/SQL runtime, or performance claim.

For the CLI-visible session lifecycle proof, `ShardLoomClient.session_cache_smoke()` runs
`session-cache-smoke --format json` and returns a typed `SessionCacheSmokeReport`. That smoke
exercises scoped SourceState, `VortexPreparedState`, OutputPlan, schema-cache, dictionary-cache,
fingerprint invalidation, scratch-buffer reuse accounting, optimizer-trace linkage, explicit close,
and cleanup evidence. It is local and claim-gated; persistent cross-process cache,
object-store/table reuse, and non-local workflow reuse remain outside this scoped session surface.

The explicit prepare-once Vortex lifecycle is available for advanced validation through a
feature-gated CLI/Python surface. Build the CLI with `--features vortex-write`, then call
`ctx.read_csv(...).prepare_vortex(workspace=...)` for a prepared source,
`ctx.read_csv(...).prepare_vortex(workspace=...).query(...).collect()` for the public
Prepare-Once First Query route,
`ctx.from_rows(...).prepare_vortex(workspace=...)`,
`ShardLoomClient.vortex_prepare(...)`, or `ctx.prepare_vortex(...)` when you intentionally need
to inspect the `UniversalIngress -> SourceState -> vortex_ingest -> VortexPreparedState` boundary:

```powershell
@"
id,label,amount
1,alpha,8
2,beta,15
"@ | Set-Content -Encoding utf8 target\vortex-ingest-source.csv

cargo run -q -p shardloom-cli --features vortex-write -- `
  vortex-prepare target\vortex-ingest-source.csv target\vortex-ingest-source.vortex `
  --allow-overwrite --format json

$env:PYTHONPATH = "python\src"
$env:SHARDLOOM_REPO_ROOT = "."
python -c "from shardloom import context; ctx=context(); r=ctx.read_csv('target/vortex-ingest-source.csv').prepare_vortex(workspace='target/shardloom-prepared', allow_overwrite=True); print(r.vortex_ingest_status, r.prepared_state_created, r.prepared_state_reuse_hit, r.prepared_state_reuse_reason, r.fallback_attempted, r.external_engine_invoked)"
```

Default CLI builds return a deterministic feature-gate blocker instead of writing an artifact. This
path is a local fixture smoke; it is not the primary user API, broad Vortex writer support,
object-store/table output support, production SQL/DataFrame support, or a performance claim.
`LazyFrame.prepare_vortex(...)` is the higher-level local `auto` source front door: it derives
`<workspace>/<source-stem>.vortex` when a workspace is supplied, calls the real Rust
`vortex-prepare` route, and exposes `prepared_state_reuse_hit`,
`prepared_state_reuse_reason`, `prepared_state_reuse_manifest_digest`, and
`prepared_state_invalidation_reason` through typed properties. It prepares the raw local source
before query operators; use `.write_vortex(...)` when the desired artifact is a query-result sink.
Generated-source `prepare_vortex(...)` uses the existing generated-source Vortex writer and returns
a `GeneratedSourceWriteReport` with `prepared_state_created` and manifest-backed reuse fields.
Repeated compatible generated-source preparation reuses the caller-owned local `.vortex` artifact
through the artifact-adjacent manifest, reports `prepared_state_reuse_hit=true`, and skips the
writer/reopen path when schema, row payload, plan, policy, and artifact fingerprints still match.
The route capability report exposes both public prepared front doors as machine-readable rows:

```python
routes = ctx.user_route_capability_report()

for row in routes.public_front_door_route_rows:
    print(row.front_door_id, row.public_user_surface, row.prepared_state_reuse_scope)
```

Those rows are route guidance and release-readiness evidence. They do not run a benchmark or allow
performance, production, or Spark-replacement claims.
The benchmark publication bundle mirrors them as `public_front_door_benchmark_rows`, where they are
route-identity rows rather than timing rows. The website uses those rows to show each public Python
prepared front door beside its owning route lane, timing boundary, reuse manifest scope, and
no-fallback evidence.
When capillary preparation is admitted, the report exposes
`vortex_capillary_preparation_prewrite_status`,
`vortex_capillary_preparation_prewrite_scheduler_applied`, and pre-write gate fields for array
build, write, reopen, and sink evidence so Python callers can see whether PulseWeave-shaped work
windows affected the local route before artifact creation.

For one concrete request, use the public workflow facade. `route()` is side-effect-free: it does
not read the input, write outputs, run SQL, or invoke external engines. `run()` and `prepare(...)`
execute only admitted ShardLoom-native wrapper paths and attach the same route metadata to the
runtime or preparation envelope:

```python
sql_route = ctx.sql("SELECT id FROM 'target/orders.csv' LIMIT 10").route()
df_route = ctx.read("target/orders.csv").select("id").limit(10).route()
execution = ctx.read("target/orders.csv").select("id").limit(10).run()
prepared = ctx.read_csv("target/orders.csv").prepare("target/orders.vortex")
native_vortex = ctx.client.public_workflow_run(
    "cli",
    input_uri="shardloom-vortex/tests/fixtures/local_primitive_struct_five.vortex",
    input_format="vortex",
    requested_output="collect",
    execution_policy="native_vortex",
    materialization_policy="zero_decode",
    evidence_level="runtime_smoke",
    bounded=True,
    vortex_primitive="filter_project",
    vortex_predicate="gte:value:3",
    vortex_columns=("metric",),
    vortex_source_order_limit=2,
    memory_gb=1,
    max_parallelism=2,
)

print(sql_route.route_id, sql_route.resolved_internal_command)
print(df_route.route_id, df_route.resolved_internal_command)
print(sql_route.fallback_attempted, sql_route.external_engine_invoked)
print(sql_route.side_effect_free, sql_route.blocker_id)
print(execution.facade_command, execution.route_id, execution.runtime_execution)
print(prepared.facade_command, prepared.route_id, prepared.preparation_included)
print(native_vortex.command, native_vortex.route_id, native_vortex.vortex_primitive)
```

For direct `.vortex` inputs, `route()` and `run()` infer the admitted primitive/provider payloads
for scoped count/filter/project/limit, no-argument row-level distinct, bounded source-order tail,
deterministic row-count
`sample(n=..., seed=...|random_state=<int>, weights="<numeric-column>", replace=False|True)` or
fractional
`sample(frac|fraction=..., seed=...|random_state=<int>, weights="<numeric-column>", replace=False|True)`,
and exact benchmark-family grouped aggregate, hash join, global top-N, cast/try-cast,
substring contains, and native
`write_vortex` sink shapes. Manual
`vortex_primitive` and `native_vortex_provider_scenario` arguments remain available on
`ctx.client.public_workflow_*` for low-level diagnostics, but normal Python/SQL facades route the
admitted shapes without requiring those flags.

Unbounded collect requests block at route admission and keep
`runtime_execution=false`, `fallback_attempted=false`, and `external_engine_invoked=false` in the
envelope. The equivalent CLI surfaces are
`shardloom route <sql|python|dataframe|cli> --format json`,
`shardloom run <sql|python|dataframe|cli> --format json`, and
`shardloom prepare <sql|python|dataframe|cli> --format json`.
Lazy DataFrame bounded `collect()` and admitted generated-source/source-free writes route through
the public `run` facade and return existing typed reports with attached `public_workflow_*` route
fields. `write_vortex(...)` remains the highest-fidelity native sink when the upstream provider
route is admitted. Exact provider-backed result summaries can export bounded `result_json` to
workspace-safe JSONL/CSV, and scoped primitive filter/project/filter-project/distinct/tail/sample row streams
can export JSONL/CSV or JSONL+CSV fanout through `native_vortex_primitive_row_export`. Broader
compatibility writes such as arbitrary `write(...)`, structured write aliases, unsupported formats,
and unsafe or non-admitted fanout return deterministic blockers until a native Vortex sink/export
route exists for the normalized plan. Native Vortex primitive and promoted provider helpers attach
the inferred route payloads to the same facade rather than relying on a separate payload-only path.

Traditional analytics compatibility inputs can also use the explicit context/session prepared route
or the lower-level client helpers. `ctx.prepare_vortex(..., workspace=...)` and
`session.prepare_vortex(..., workspace=...)` return a route handle for
`compatibility_import_certified -> prepared_vortex`; `query(...).collect()` runs a single prepared
query and `run_batch([...])` runs a prepared scenario batch. The first compatible call invokes
`traditional-analytics-prepare-batch-run`, prepares the local fact/dimension inputs once into
prepared Vortex artifacts, and writes a caller-owned workspace manifest. Later compatible calls
reuse that manifest and run `traditional-analytics-vortex-batch-run` directly over the existing
artifacts when source, artifact, and prepare-policy fingerprints match. The returned envelope keeps
`prepare_batch_*`, source-state reuse, fallback, claim-boundary, `prepared_state_reuse_hit`,
`prepared_state_reuse_reason`, `prepared_state_reuse_manifest_digest`, and `invalidation_reason`
fields visible:

```python
import shardloom as sl

ctx = sl.context()
prepared = ctx.prepare_vortex(
    "fact.csv",
    dim="dim.csv",
    workspace="target/prepare-batch",
    input_format="csv",
    evidence_level="certified",
)
result = prepared.run_batch(["selective filter", "filter + projection + limit"])

print(prepared.route_fields())
print(result.batch.field("prepare_batch_preparation_included_in_batch_timing"))
print(result.batch.field("source_state_reuse_status"))
print(result.batch.field("prepare_batch_lifecycle_status"))
print(result.prepared_state_reuse_hit, result.prepared_state_reuse_reason)
print(result.batch.field("scenario_selective-filter_prepared_native_vortex_lifecycle_status"))
print(result.fallback_attempted, result.external_engine_invoked)
```

This is a scoped local runtime route for avoiding repeated compatibility preparation inside a batch.
`PreparedVortexBatchResult.lifecycle_status`,
`PreparedVortexBatchResult.lifecycle_output_status`, and
`PreparedVortexBatchResult.lifecycle_no_standalone_lane` expose the combined route lifecycle
posture. `ExecutionResultEnvelopeView.prepared_native_vortex_lifecycle_status` and related output/
no-standalone accessors expose the per-scenario lifecycle fields when a typed execution result
contains them.
Use `ShardLoomClient.traditional_analytics_prepare_batch_run(...)`,
`prepare_and_run_traditional_analytics_vortex_batch(...)`, or
`prepare_traditional_analytics_vortex_artifacts(...)` only when the caller needs lower-level CLI
control or explicit artifact lifecycle management across later commands. This is not a native Python binding,
persistent cache, object-store/table runtime, package-readiness claim, or performance claim.

For existing native `.vortex` fact/dimension artifacts, use the route-level native handle when you
want the same benchmark-family runtime path rather than isolated primitive helpers:

```python
native = ctx.native_vortex_route(
    "fact.vortex",
    "dim.vortex",
    execution_mode="native_vortex",
    memory_gb=4,
    max_parallelism=1,
)

result = native.query("selective filter").collect()
sink = native.query("selective filter").write_vortex("target/native-result")

print(native.route_fields())
print(result.field("selected_execution_mode"))
print(result.fallback.attempted)
```

`read_vortex(...).count/filter/select/limit/distinct/tail/sample/collect`, admitted grouped
aggregate/join/top-N/cast/contains chains, and native `write_vortex` sinks route through the public native Vortex facade when
their shape has a certificate-backed provider route. `native_vortex_route(...)` remains the
explicit route-comparable surface for the production provider facade
(`vortex-production-runtime-run`) and the lower-level benchmark-compatible helpers
(`traditional-analytics-vortex-run` / `traditional-analytics-vortex-batch-run`); it keeps source,
execution mode, scenario/operator, memory/parallelism hints, result sink, and no-fallback evidence
visible.

Engine intent is explicit. `engine="auto"` selects the current bounded snapshot
batch path when allowed; `live` selects the CG-22 in-memory fixture path for
bounded/unbounded change streams; `hybrid` selects the CG-22 declared Vortex-base
plus in-memory hot-delta fixture for snapshot/bounded base overlays:

```python
import shardloom as sl

ctx = sl.context(engine="live")
selection = ctx.engine_selection(
    boundedness="unbounded",
    update_mode="append-only",
    output_mode="changelog",
)
matrix = ctx.engine_capability_matrix()

print(ctx.engine)
print(selection.selection_status)
print(selection.selected_engine_mode)
print(selection.rejection_reasons)
print(matrix.engine_modes)
print(matrix.live_hybrid_claim_blocked_count)
print(matrix.live_hybrid_fabric_gate_rows)
print(matrix.live_hybrid_fabric_gate_claim_gate_status)
print(matrix.live_hybrid_fabric_gate_no_fallback_no_external_engine)
```

These calls do not execute workloads, probe brokers, write checkpoints, invoke
external engines, or attempt fallback. They expose the same CG-22 contract as
`shardloom engine-selection-plan`, `shardloom engine-capability-matrix`, and
`shardloom capabilities engines`.

`ctx.engine_capability_matrix()` also exposes the GAR-0034-A live/hybrid fabric
freshness gate. The gate keeps broker, state-store, object-store, catalog,
production freshness, and exactly-once claims blocked unless future
workload-scoped evidence promotes them, while preserving
`fallback_attempted=false`, `external_engine_invoked=false`, and
`claim_gate_status=not_claim_grade`.

The executable live surface is intentionally narrower: a deterministic
in-memory fixture for filter, project, count, count_where, and group_count. It
does not read brokers or files and does not write checkpoints, but it does emit
freshness, state, continuous-view, execution, and Native I/O certificate fields:

```python
contract = ctx.live_change_contract_plan()
fixture = ctx.live_fixture_run("group-count", "metric")

print(contract.change_record_fields)
print(contract.operations)
print(fixture.output_rows)
print(fixture.all_certified)
print(fixture.fallback_attempted)
```

Equivalent CLI commands:

```powershell
shardloom live-change-contract-plan --format json
shardloom live-fixture-run group-count metric --format json
```

The executable hybrid surface is also fixture-scoped. It merges declared local
Vortex base rows with deterministic hot deltas, applies tombstones/deletion
vectors in memory, and emits delta-overlay, hot/cold contribution,
micro-segment flush, layout-health, freshness, execution, and Native I/O
evidence without reading or writing data:

```python
hybrid = sl.context(engine="hybrid").hybrid_overlay_run("group-count", "metric")

print(hybrid.output_rows)
print(hybrid.layout_health_status)
print(hybrid.all_certified)
print(hybrid.write_io)
```

Equivalent CLI command:

```powershell
shardloom hybrid-overlay-run group-count metric --format json
```

The first CG-23 REST/API surface is contract-first. It checks the versioned
OpenAPI `/v1` contract and the discovery-mode `serve` contract without starting
a server, opening a listener, probing datasets, touching object stores, or
executing queries:

```python
api = ctx.rest_api_contract_plan()
discovery = ctx.serve_discovery_contract()
preview = ctx.rest_api_plan_preview("certified-local-batch")
lifecycle = ctx.rest_api_local_lifecycle("certified-local-batch")
events = ctx.rest_api_event_stream("certified-live-fixture")
security = ctx.rest_api_security_governance("safe-local-default")
data_plane = ctx.rest_api_data_plane("standards-matrix")

print(api.openapi_contract_path)
print(api.represented_resources)
print(api.discovery_endpoint_paths)
print(api.rest_runtime_unsupported_rows)
print(api.rest_runtime_unsupported_claim_gate_status)
print(api.rest_runtime_no_server_no_fallback_no_external_engine)
print(api.server_started)
print(discovery.server_mode)
print(discovery.contract_only)
print(preview.plan_handle)
print(preview.stage_statuses)
print(preview.problem_details_emitted)
print(lifecycle.lifecycle_status)
print(lifecycle.result_ref)
print(lifecycle.result_policies)
print(lifecycle.arrow_ipc_materialization)
print(lifecycle.fallback_attempted)
print(events.event_stream_status)
print(events.delivery_protocols)
print(events.event_types)
print(events.asyncapi_contract_path)
print(events.broker_io)
print(security.governance_status)
print(security.auth_postures)
print(security.api_scopes)
print(security.mcp_tools)
print(security.evidence_model_signals)
print(security.secrets_redacted)
print(data_plane.transfer_modes)
print(data_plane.preferred_large_payload_modes)
print(data_plane.standards_names)
print(data_plane.flight_adbc_required_for_basic_local_use)
```

Equivalent CLI commands:

```powershell
shardloom rest-api-contract-plan --format json
shardloom rest-api-plan-preview certified-local-batch --format json
shardloom rest-api-plan-preview unsupported-operator --format json
shardloom rest-api-local-lifecycle certified-local-batch --format json
shardloom rest-api-local-lifecycle blocked-uncertified --format json
shardloom rest-api-event-stream certified-live-fixture --format json
shardloom rest-api-event-stream broker-requested --format json
shardloom rest-api-security-governance safe-local-default --format json
shardloom rest-api-security-governance destructive-policy-required --format json
shardloom rest-api-security-governance agent-mcp-discovery --format json
shardloom rest-api-data-plane artifact-reference-default --format json
shardloom rest-api-data-plane flight-ticket-requested --format json
shardloom rest-api-data-plane adbc-endpoint-requested --format json
shardloom rest-api-data-plane standards-matrix --format json
shardloom serve --mode discovery --format json
```

The GAR-0035-A REST runtime unsupported gate keeps HTTP listener, remote execution, Flight/ADBC
transport, external broker integration, and dependency-expanded server claims blocked. The REST
contract remains a checked-in OpenAPI/reporting surface until separate workload, server lifecycle,
security, Native I/O, execution-certificate, and no-fallback evidence exists.

Lazy workflow planning is also available without adding pandas, Polars, Spark,
DataFusion, or any other execution dependency:

```python
import shardloom as sl

ctx = sl.context()
workflow = (
    ctx.read_vortex("orders.vortex")
    .filter("gte:value:3")
    .select("order_id", "amount")
    .limit(10)
)

plan = workflow.plan()
explain = workflow.explain()
estimate = workflow.estimate()
certification = workflow.certify()
unsupported = workflow.unsupported_report()

print(workflow.operation_summary)
print(plan.field("plan_only"))
print(explain.status)
print(estimate.status)
print(certification.fallback_attempted)
print(unsupported.fallback_attempted)
```

The same top-level helpers are exported as `sl.read_vortex`, `sl.read_csv`,
`sl.read_json`, `sl.read_parquet`, `sl.read_arrow_ipc`, `sl.read_avro`, and
`sl.read_orc`. Most helper chains
still declare sources and transformations only. `plan()`, `explain()`,
`estimate()`, `certify()`, and
`unsupported_report()` are explicit report calls over CLI JSON surfaces; they do
not read input files, infer schemas, materialize rows, probe object stores,
write output, or invoke fallback engines.

## Public Local Runtime: Universal Ingest Into A Vortex Middle

ShardLoom's Python front door is format-neutral at the execution boundary. `ctx.read(path)` and
the explicit `ctx.read_csv(...)`, `ctx.read_json(...)`, `ctx.read_parquet(...)`,
`ctx.read_arrow_ipc(...)`, `ctx.read_avro(...)`, `ctx.read_orc(...)`, and
`ctx.read_vortex(...)` helpers are input adapters. They do not create separate CSV, JSON,
Parquet, Arrow, Avro, ORC, SQL, or DataFrame execution stacks.

For local compatibility inputs, admitted public workflows now normalize through a caller-local
Vortex prepared artifact under `.shardloom/prepared/*.vortex`, then execute the admitted native
Vortex primitive/provider route. Direct decoded `local-source-runtime` remains available only as
an internal diagnostic smoke safeguard. Public `collect()`, `count()`, `preview()`, `head()`, `take()`, and
admitted `ctx.sql(...)` local-source reads must either enter the Vortex-prepared/native path or
return a deterministic unsupported report. They must not silently decode or materialize local
compatibility files as the runtime middle.

The invariant on admitted public local workflows is:

```text
input adapter -> SourceState -> VortexPreparedState or native Vortex input -> ShardLoom native route -> typed result/sink evidence
fallback_attempted=false
external_engine_invoked=false
```

Feature-gated structured adapters such as Parquet, Arrow IPC, Avro, and ORC still require the
matching build/runtime feature. When an adapter, operator, or sink is not enabled or not admitted,
the Python client returns the CLI unsupported envelope with a stable blocker id and next action.
That is intentional: unsupported work fails closed instead of using pandas, Polars, DuckDB, Spark,
DataFusion, or another engine.

A normal local Python use looks like this:

```python
import shardloom as sl

ctx = sl.context()
orders = ctx.read("target/orders.csv")

result = (
    orders
    .filter(sl.col("amount") >= 10)
    .select("id", "amount", "status")
    .limit(10)
    .collect()
)

print(result.output_row_count)
print(result.first_result_row)
print(result.prepared_vortex_path)
print(result.vortex_ingest_performed)
print(result.fallback_attempted, result.external_engine_invoked)
```

The equivalent scoped SQL front door uses the same lifecycle after source parsing:

```python
sql_result = ctx.sql(
    "SELECT id, amount, status FROM 'target/orders.csv' "
    "WHERE amount >= 10 LIMIT 10"
).collect()

print(sql_result.prepared_vortex_path)
print(sql_result.fallback_attempted, sql_result.external_engine_invoked)
```

Direct native Vortex input skips compatibility preparation and starts at the Vortex boundary:

```python
vortex_result = (
    ctx.read_vortex("target/orders.vortex")
    .filter(sl.col("amount") >= 10)
    .select("id", "amount", "status")
    .limit(10)
    .collect()
)

print(vortex_result.native_vortex_capability_status)
print(vortex_result.fallback_attempted, vortex_result.external_engine_invoked)
```

Exact benchmark-family provider shapes are admitted through the same public facade when the native
provider route is available: grouped count/sum, null-heavy grouped count/sum, hash join with a
declared right Vortex input, global top-N, clean/cast/filter, malformed timestamp cast, substring
contains, and native Vortex result sinks. The current exact-route inventory lives in
`docs/architecture/v1-vortex-runtime-scope.md` and the machine-readable capability reports.
General SQL/DataFrame parity, arbitrary expression trees, arbitrary joins, broad schema/profile
materialization, and broad remote/table exports are not implied by those exact routes.

Compatibility sinks such as JSONL/CSV/Parquet/Arrow IPC/Avro/ORC are admitted for scoped local
workflows after Vortex preparation or native Vortex input and declared output replay evidence.
`write_vortex(...)` remains the highest-fidelity local Vortex sink route for admitted native-provider
workflows. If a sink shape is not admitted, call it with `check=False` to inspect the deterministic
blocker without raising:

```python
result = orders.write_jsonl("target/orders.jsonl", allow_overwrite=True, check=False)
print(result.output_path)
print(result.result_replay_verified)
print(result.fallback_attempted, result.external_engine_invoked)
```

The lower-level `client.sql_local_source_smoke(...)` helper remains documented only for internal
fixture-smoke and regression work. It is not the public product route and should not be used in
normal application examples.

Evidence-aware optimizer traces are planned as `GAR-PERF-2B`, not current Python runtime support. A
future Python `explain()` trace should expose optimizer rule status, before/after plan digests,
rewrite safety, evidence preservation, no-fallback fields, and claim gates without implying broad
SQL/DataFrame execution or Polars/DataFusion optimizer parity.

Reusable I/O state and broad cross-format fanout are planned as `GAR-IOREUSE-1`. Public local
compatibility workflows no longer use a direct local-source fanout/write route as a product runtime:
they prepare through Vortex first, then emit declared local sink replay evidence. Generated-source
helpers keep their separate local-output fanout surfaces because those rows are produced by explicit
source-free/generated-source commands, not by decoding a local compatibility file as the runtime
middle.
Current typed result objects expose scoped `SourceState`, `VortexPreparedState`, and `OutputPlan`
evidence where the CLI emits it; future Python capability/write views may broaden cache
invalidation, reuse levels, persistent OutputPlan reuse, and claim-grade replay/fidelity evidence.
Input and output formats remain decoupled, and reuse evidence will not imply performance,
production, object-store/lakehouse, Foundry, or SQL/DataFrame support.

Unsupported workflow affordances are explicit report surfaces too. These calls show how familiar
pandas/Arrow/DataFrame/notebook methods either use admitted bounded local-source/materialized-input
shapes or fail closed when the requested operation is outside that scope:

Unsupported rows expose stable evidence fields such as `blocked.required_evidence`,
`blocker_id`, `required_evidence`, and `suggested_next_action` for agents.

```python
import shardloom as sl

ctx = sl.context()
workflow = ctx.read_csv("events.csv").filter("amount > 0")
selected_workflow = workflow.select("customer_id", "amount")

reports = [
    sl.from_pandas(object()),
    sl.from_arrow_table(object()),
    sl.from_arrow_ipc("events.arrow"),
    workflow.to_pandas(),
    workflow.to_arrow_table(),
    workflow.to_arrow_ipc(),
    workflow.to_numpy(),
    workflow.with_column("event_date", "to_date(ts)"),
    selected_workflow.group_by("customer_id", "region").agg(total="sum(amount)"),
    selected_workflow.group_by("customer_id").agg(total="sum(amount)"),
    selected_workflow.agg("count(*)"),
    workflow.sort("event_date"),
    workflow.data_quality_check("regex:id"),
    sl.read_csv("events.data").display(),
    ctx.sql_parse("select * from events"),
    ctx.sql_bind("select * from events"),
    ctx.sql_plan("select * from events"),
    ctx.sql_execute("select * from events"),
]

for report in reports:
    print(getattr(report, "operation", type(report).__name__))
    print(getattr(report, "blocker_id", None))
    print(getattr(report, "required_evidence", ()))
    print(getattr(report, "suggested_next_action", None))
    print(
        getattr(report, "runtime_execution", None),
        getattr(report, "data_read", None),
        getattr(report, "write_io", None),
    )
```

Unsupported reports above are generated through `workflow-unsupported-plan` and return
`status="unsupported"` with `fallback_attempted=false`; admitted reports preserve the same
no-fallback fields on their success evidence. The methods do not
use pandas, pyarrow, or numpy as execution engines, parse SQL, execute
unsupported DataFrame expressions, render broad notebook runtime output, invoke Foundry/model
services, or use another engine as fallback. Valid pandas/Arrow inputs are treated as explicit
materialized snapshots that lower to generated-source user rows, not as hidden external execution.

The DataFrame-style surface also has a typed method capability matrix. Use it
when a wrapper, notebook, or agent needs to know which familiar method names are
lazy declarations, which have scoped runtime-smoke support, which are unsupported diagnostics, and
which evidence gates bound each method:

```python
import shardloom as sl

ctx = sl.context()
matrix = ctx.capabilities().dataframe_method_matrix

print(matrix.row_order)
print(matrix.plan_only_methods)
print(matrix.unsupported_methods)
print(matrix.all_no_fallback_no_external_engine)

join = matrix.row("join")
print(join.support_status)
print(join.blocker_id)
print(join.required_evidence)
print(join.claim_boundary)
```

This matrix is still claim-safe, but its statuses should be read through the Vortex-middle contract.
Local compatibility rows are not successful public runtime routes merely because a lower-level smoke
command exists. Terminal methods are one of: side-effect-free lazy declarations, production-admitted
local workflows that normalize through Vortex preparation or native Vortex input, source-free
GeneratedSource local-output rows, internal smoke safeguards, or deterministic unsupported reports.

For local compatibility inputs, admitted `collect()`, `count()`, `preview()`, `head()`, `take()`,
schema/data-quality summaries, bounded decoded materialization, local compatibility writes,
compatibility fanout, quarantine sinks, and exact benchmark-family provider shapes enter the same
Vortex-prepared/native route described above. Profile summaries and broad production/export
semantics require an admitted route contract: native `.vortex` metadata profiles are admitted for
base read/select/limit shapes, and scoped `describe(...)` lowers to that same metadata-first
profile route. Transformed row profiling, pandas-style percentile/options summaries, and broad
production profiling remain blocked until a native Vortex materialization/profile contract admits
them. Alias rows such as
`project`, `where`, `groupby`, `order_by`,
`sort_values`, `merge`, `nlargest`, scoped `tail`, and scoped deterministic `sample` are useful only when their normalized operation shape maps to
an admitted native route; otherwise they return the matching unsupported report before data is read.

Generated-source rows such as `ctx.from_rows(...)`, `ctx.range(...)`, `ctx.sequence(...)`, and
source-free SQL have their own local output contracts. Those are not proof that local CSV/JSON/etc.
compatibility files can use direct decoded sinks as a product runtime.

`schema_contract(...)` and `validate_schema(...)` are bounded local schema evidence surfaces after
Vortex preparation. They are not broad schema registry, table constraint manager, or object-store/
lakehouse enforcement surfaces.
`profile(...)` is admitted for metadata-first native `.vortex` base read/select/limit profiles and
otherwise returns deterministic blockers until a route-specific profile/materialization contract
exists. It is not a hidden pandas/Polars profiler, resource tracer, performance claim, or
production observability surface.
`quarantine(...)` is admitted for bounded local checks and optional local sink replay evidence. It is
not object-store/table quarantine, production remediation, or a broad data-governance engine.

When the question is broader than one DataFrame method, use the front-door parity matrix. It
separates workflows that already lower SQL, Python, and DataFrame-style code to the same ShardLoom
runtime path from the gaps that still block arbitrary SQL/Python/DataFrame flexibility and
performance-equivalence claims. The scoped v1 boundary is owned by
`docs/architecture/v1-front-door-runtime-scope.md`:

```python
parity = ctx.front_door_parity_matrix()

print(parity.scoped_local_front_door_parity_supported)
print(parity.flexible_anything_claim_allowed)
print(parity.performance_equivalence_claim_allowed)
print(parity.row("local_file_filter_project_limit").shared_runtime_path)
print(parity.row("arbitrary_sql_python_dataframe_breadth").blocker_id)
```

Use the semantic surface matrix when the question is "which API/SQL semantic family is covered?"
instead of "which route is selected?" It is the agent-facing companion to the human parity doc:

```python
semantic = ctx.front_door_semantic_surface_matrix()

print(semantic.dataframe_subset_claim_statement)
print(semantic.sql_claim_statement)
print(semantic.pandas_compatible_claim_allowed)
print(semantic.ansi_sql_compliant_claim_allowed)
print(semantic.row("dataframe_materialization").claim_boundary)
```

Scoped local-file rows are admitted only when they normalize through Vortex preparation or start
from native Vortex input and then match an admitted primitive/provider route. Generated-output rows
remain separate source-free local-output contracts. Bounded schema/data-quality previews and decoded
Python/pandas/Arrow/NumPy materialization are explicit gap rows until native Vortex-derived evidence
and export/materialization contracts close them. General Vortex workflows, object-store/lakehouse/
table I/O, arbitrary SQL/Python/DataFrame breadth, and cross-front-door performance equivalence
remain explicit gap rows until correctness, Native I/O, execution-certificate, no-fallback, and
benchmark evidence closes them.

The v1 Vortex runtime scope is owned by `docs/architecture/v1-vortex-runtime-scope.md`. Use
`ctx.local_vortex_primitive_route_report()` for the feature-gated local Vortex primitive route
ids, CLI commands, materialization boundaries, and no-fallback evidence posture; broad object-store
Vortex, table/catalog Vortex, generalized Source/Sink, and broad Vortex SQL/DataFrame support remain
outside that scope.
Use `ctx.native_vortex_provider_route_certificate_report()` for the exact feature-gated native
Vortex provider routes that admit benchmark-family grouped aggregation, hash join, global top-N,
cast/try-cast, substring contains, and native `write_vortex` sink shapes from Python and SQL.

The v1 SourceState/prepared-state scope is owned by
`docs/architecture/v1-source-prepared-state-scope.md`. Use
`ctx.source_prepared_state_scope_report()` to inspect the
`UniversalIngress -> SourceState -> vortex_ingest -> VortexPreparedState` route, the direct
transient boundary, reuse/invalidation case ids, golden fixture refs, and required benchmark
evidence fields. This report is local and claim-gated; it is not a global hidden cache, external
cache service, object-store/table prepared-state reuse, broad non-local preparation, or performance
claim.

Package, DataFrame, and notebook readiness are also exposed as a separate typed
matrix so local install smoke is not confused with public package publication or
broad runtime support:

```python
readiness = ctx.dataframe_notebook_package_readiness()

print(readiness.schema_version)
print(readiness.local_install_smoke_supported)
print(readiness.package_publication_ready)
print(readiness.dataframe_runtime_supported)
print(readiness.notebook_runtime_supported)
print(readiness.all_rows_no_fallback_no_external_engine)

publication = readiness.row("public_package_publication")
print(publication.support_status)
print(publication.blocker_id)
print(publication.required_evidence)
print(publication.claim_boundary)
```

This readiness matrix is report-only capability posture. It does not publish to
PyPI/TestPyPI/Conda/Homebrew, import notebook or DataFrame dependencies, render
rich notebook output, execute broad DataFrame plans, call package repositories,
or invoke external engines. Public package publication, broad DataFrame runtime,
and notebook runtime remain blocked until release and execution evidence gates
pass.

The CG-21 ETL workflow surface also has a compact typed matrix for current local
workflow posture. Use it when a wrapper, notebook, or agent needs one place to
show which user workflows are ready or smoke-supported, which APIs are
report-only, and which production/runtime claims remain blocked:

```python
matrix = ctx.etl_workflow_matrix()

print(matrix.schema_version)
print(matrix.supported_local_rows)
print(matrix.report_only_rows)
print(matrix.blocked_rows)
print(matrix.all_no_fallback_no_external_engine)

blocked = matrix.row("object_store_runtime")
print(blocked.status)
print(blocked.blocker_id)
print(blocked.claim_boundary)
```

This matrix is side-effect-free capability posture. It does not run production
ETL, SQL/DataFrame execution, object-store/lakehouse runtime, Foundry runtime,
external engine execution, or package publication, and it does not create
performance or Spark-displacement claims.

`GAR-0037-A` adds a wrapper/connector implementation registry on the API-surface
capability view. Use it when a client, adapter, agent, or public docs page needs
to distinguish the current source-tree Python wrapper from planned or blocked
ecosystem connectors:

```python
caps = ctx.capabilities()
registry = caps.wrapper_connector_registry
# Or: registry = ctx.wrapper_connector_registry()

print(registry.schema_version)
print(registry.ready_local_count)
print(registry.report_only_count)
print(registry.blocked_count)
print(registry.all_rows_no_fallback_no_external_engine)

python = registry.row("python_cli_json_client")
sqlalchemy = registry.row("sqlalchemy")

print(python.support_status)
print(python.explicit_execution_available)
print(sqlalchemy.support_status)
print(sqlalchemy.deterministic_diagnostic_code)
print(sqlalchemy.claim_boundary)
```

The registry is capability posture, not connector implementation. It does not
add generated clients, DB-API, SQLAlchemy, Ibis, dbt, Airflow, Dagster, Prefect,
MCP, Flight SQL, ADBC, JDBC/ODBC, BI, Grafana, Foundry package, REST server,
dependency expansion, network listener, external engine execution, or fallback.
Rows preserve `fallback_attempted=false`, `external_engine_invoked=false`, and
`claim_gate_status=not_claim_grade`.

Source-free generated-output APIs are tracked under `GAR-GEN-1`. The full
contract is exposed through capability views as `generated_source_contract`, and
the per-API admission matrix is exposed as `generated_source_api_admission`.
`GAR-NOVEL-1A` also exposes `generated_source_evidence_alignment`, which ties the same
GeneratedSourceCertificate rows to report-only OpenLineage, OpenTelemetry, Bayesian-confidence,
and Foundry generated-output boundary refs without enabling exporters or platform runtime.
Scoped local JSONL/CSV smoke paths are runtime-supported for caller-provided rows,
Python literal tables, Python calendar/date dimensions, ShardLoom-native
range/sequence generators, SQL `VALUES`, SQL literal `SELECT`, and SQL
`generate_series`/`range`. Broader SQL/DataFrame
forms remain report-only unless a later evidence-backed slice admits them:

```python
caps = ctx.capabilities()
contract = caps.python.generated_source_contract
admission = caps.python.generated_source_api_admission
alignment = caps.python.generated_source_evidence_alignment
lineage = ctx.observability().openlineage_facet_mapping
telemetry = ctx.observability().opentelemetry_trace_export_contract

print(contract.schema_version)
print(contract.case_order)
print(contract.no_dataset_smoke_separate_from_generated_output)
print(contract.all_no_fallback_no_external_engine)
print(admission.row("python_ctx_from_rows").support_status)
print(admission.row("python_ctx_range").runtime_execution)
print(admission.row("python_ctx_sequence").runtime_execution)
print(admission.row("python_ctx_literal_table").support_status)
print(admission.row("python_ctx_calendar").runtime_execution)
print(admission.row("sql_values").support_status)
print(admission.row("sql_literal_select").runtime_execution)
print(admission.row("sql_generate_series_range").runtime_execution)
print(admission.all_no_fallback_no_external_engine)
print(alignment.schema_version)
print(alignment.openlineage_export_enabled)
print(alignment.opentelemetry_network_exporter_enabled)
print(alignment.row("foundry_generated_output").foundry_boundary_ref)
print(lineage.schema_version)
print(lineage.row("generated_source").facet_name)
print(lineage.all_rows_report_only)
print(lineage.all_no_fallback_no_external_engine)
print(telemetry.schema_version)
print(telemetry.row("operator_compute").timing_fields)
print(telemetry.network_exporter_enabled)
print(telemetry.no_export_side_effects)
```

The universal compatibility view also projects the same source-free generated-output posture so
callers do not need to join GAR-GEN docs by hand:

```python
compatibility = ctx.compatibility_scoreboard()
generated = compatibility.source_free_generated_output_contract

print(generated.schema_version)
print(generated.no_dataset_smoke_separate)
print(generated.local_output_only)
print(generated.output_certificate_required)
print(generated.row("python_ctx_from_rows").support_status)
print(generated.row("sql_values").support_status)
print(generated.row("local_output_only_generated_source_posture").blocker_id)
print(generated.all_no_fallback_no_external_engine)
```

This compatibility contract is still a capability map. It can say local JSONL/CSV generated-output
smokes exist for user rows, literal tables, calendar/date dimensions, range, sequence, scoped
generated-row projection/literal `with_column`, SQL `VALUES`, SQL literal `SELECT`, and SQL
`generate_series`/`range`, but it keeps
broader SQL runtime, broad DataFrame generated expressions, object-store/lakehouse output, and
Foundry generated-output runtime as report-only or blocked.

The supported user-row local smoke uses Python rows supplied by the caller,
writes a local JSONL/CSV file, and returns generated-source/output evidence:

```python
from shardloom import context

ctx = context()
report = ctx.from_rows(
    [
        {"id": 1, "label": "alpha"},
        {"id": 2, "label": "beta"},
    ]
).write("target/generated-reference.jsonl")

print(report.generated_source_kind)
print(report.generated_source_row_count)
print(report.generated_source_certificate_status)
print(report.output_native_io_certificate_status)
print(report.fallback_attempted)
print(report.external_engine_invoked)
print(report.claim_gate_status)
```

The same `GeneratedRowsSource` can now perform a narrow source-free row transform before the write.
This is intentionally limited to projection plus deterministic literal `with_column` values, then
the transformed rows still pass through ShardLoom's generated-source local-output command:

```python
transformed = (
    ctx.from_rows(
        [
            {"id": 1, "label": "alpha"},
            {"id": 2, "label": "beta"},
        ]
    )
    .with_column("segment", "lit('north')")
    .select("id", "segment")
    .write("target/generated-reference-transformed.jsonl", allow_overwrite=True)
)

print(transformed.generated_source_kind)
print(transformed.generated_source_row_count)
print(transformed.generated_source_certificate_status)
print(transformed.output_native_io_certificate_status)
print(transformed.fallback_attempted)
print(transformed.external_engine_invoked)
print(transformed.claim_gate_status)
```

This slice is not a broad DataFrame runtime. `with_column` accepts only `lit(...)` expressions or
direct Python bool/int/float literals, `select` only projects existing generated-row columns, and
unsupported expressions fail before execution rather than falling back to pandas, Polars, Spark,
DataFusion, DuckDB, or another engine.

Equivalent CLI command:

```powershell
shardloom generated-source-user-rows target\generated-reference.jsonl id:int64,label:utf8 "id=1,label=alpha;id=2,label=beta" --format json
```

The supported literal-table helper uses the same local generated-source write path while reporting
`generated_source_kind=literal_table`:

```python
literal_report = ctx.literal_table(
    [
        {"code": "A", "weight": 1.5},
        {"code": "B", "weight": 2.0},
    ]
).write("target/generated-literal-table.jsonl", allow_overwrite=True)

print(literal_report.generated_source_kind)
print(literal_report.generated_source_row_count)
print(literal_report.claim_gate_status)
```

The calendar/date-dimension helper generates deterministic local rows in Python, writes JSONL
through the same ShardLoom generated-source command, and reports
`generated_source_kind=calendar`:

```python
calendar_report = ctx.calendar(
    "2026-05-18",
    "2026-05-21",
    column="dt",
).write("target/generated-calendar.jsonl", allow_overwrite=True)

print(calendar_report.generated_source_kind)
print(calendar_report.generated_source_row_count)
print(calendar_report.claim_gate_status)
```

The supported engine-native range smoke is separate. It generates deterministic
`int64` rows inside ShardLoom, writes local JSONL/CSV, and emits the same
generated-source/output/no-fallback evidence family. `limit(...)`, `head(...)`, and `take(...)`
adjust the range bounds before invoking the same engine-native range/sequence smoke; they do not
materialize rows in Python:

```python
range_report = ctx.range(0, 50, column="id").limit(5).write(
    "target/generated-range.jsonl",
    allow_overwrite=True,
)

print(range_report.generated_source_kind)
print(range_report.generated_source_range_start)
print(range_report.generated_source_range_end)
print(range_report.generated_source_range_step)
print(range_report.generated_source_row_count)
print(range_report.claim_gate_status)
```

Equivalent CLI command:

```powershell
shardloom generated-source-range target\generated-range.jsonl 0 5 --column id --format json
```

The supported engine-native sequence smoke uses the same integer generator contract while reporting
`generated_source_kind=sequence`. It is scoped to local JSONL/CSV output and does not admit broader
DataFrame generation:

```python
sequence_report = ctx.sequence(0, 50, column="id").take(5).write(
    "target/generated-sequence.jsonl",
    allow_overwrite=True,
)

print(sequence_report.generated_source_kind)
print(sequence_report.generated_source_range_start)
print(sequence_report.generated_source_range_end)
print(sequence_report.generated_source_range_step)
print(sequence_report.generated_source_row_count)
print(sequence_report.claim_gate_status)
```

Equivalent CLI command:

```powershell
shardloom generated-source-sequence target\generated-sequence.jsonl 0 5 --column id --format json
```

The supported source-free SQL smokes parse a deliberately tiny SQL subset inside ShardLoom and write
local JSONL/CSV with generated-source/output/no-fallback evidence. SQL `VALUES` uses generated column
names, literal `SELECT` accepts `AS` aliases, and `SELECT * FROM generate_series/range(...)`
creates an integer source-free table with range evidence:

```python
values_report = ctx.sql_values("VALUES (1, 'alpha'), (2, 'beta')").write(
    "target/generated-sql-values.jsonl",
    allow_overwrite=True,
)
select_report = ctx.sql_literal_select(
    "SELECT 1 AS id, 'alpha' AS label, true AS active"
).write("target/generated-sql-select.jsonl", allow_overwrite=True)
ctx_sql_report = ctx.sql("SELECT 2 AS id, 'beta' AS label").write(
    "target/generated-sql-from-context.jsonl",
    allow_overwrite=True,
)
series_report = ctx.sql("SELECT * FROM generate_series(0, 4)").write(
    "target/generated-sql-series.jsonl",
    allow_overwrite=True,
)
range_topn_report = (
    ctx.range(1, 8, column="id")
    .filter(sl.col("id") >= 3)
    .with_column("doubled", sl.col("id") * 2)
    .sort("doubled", descending=True)
    .limit(2)
    .write("target/generated-range-topn.jsonl", allow_overwrite=True)
)
range_fanout_report = (
    ctx.range(1, 8, column="id")
    .filter(sl.col("id") >= 3)
    .with_column("doubled", sl.col("id") * 2)
    .sort("doubled", descending=True)
    .limit(2)
    .fanout(
        {
            "jsonl": "target/generated-range-topn.jsonl",
            "csv": "target/generated-range-topn.csv",
        },
        allow_overwrite=True,
    )
)

print(values_report.generated_source_kind)
print(values_report.generated_source_row_count)
print(select_report.generated_source_kind)
print(select_report.claim_gate_status)
print(ctx_sql_report.generated_source_kind)
print(series_report.generated_source_kind)
print(series_report.generated_source_range_end_inclusive)
print(range_topn_report.sql_source_free_top_n_runtime_execution)
print(range_topn_report.sql_source_free_sort_keys)
print(range_fanout_report.output_route)
print(range_fanout_report.fanout_output_count)
print(range_fanout_report.fanout_result_reuse_hit)
```

Equivalent CLI command:

```powershell
shardloom generated-source-sql target\generated-sql-values.jsonl "VALUES (1, 'alpha'), (2, 'beta')" --format json
shardloom generated-source-sql target\generated-sql-series.jsonl "SELECT * FROM generate_series(0, 4)" --format json
```

This SQL smoke accepts only source-free literal `SELECT` expressions and `VALUES` tuples over int64,
finite float64, bool, and single-quoted UTF-8 string literals, plus `SELECT * FROM
generate_series(start, end[, step])` and `SELECT * FROM range(start, end[, step])` over int64
arguments. `generate_series` uses an inclusive end, while `range` uses the same exclusive-end
semantics as `ctx.range(...)`. The range SQL subset also admits scoped int64 projections,
single-branch int64 `CASE`, one range-column filter, `ORDER BY` over the range source column or
projected int64 aliases, and `LIMIT`, so fluent
`ctx.range(...).filter(...).with_column(...).sort(...).limit(...).write(...)` lowers through the
same generated-source SQL smoke; the generated range builder also accepts `project`,
`with_columns`/`assign`, and `order_by`/`sort_by`/`sort_values` aliases over those same operations.
Source-free top-N reports
`sql_source_free_order_by_runtime_execution`, `sql_source_free_top_n_runtime_execution`,
`sql_source_free_sort_keys`, `sql_source_free_sort_direction`,
`sql_source_free_sort_operator_family`, and `sql_source_free_top_n_limit` alongside projection,
filter, and limit evidence. `ctx.sql(...).write(...)` dispatches those source-free forms through the
public workflow `run` facade to the generated-source SQL runtime, and `ctx.sql(...).fanout(...)`
dispatches source-free generated forms through the same public facade and generated-source fanout
contract. Generated-source fanout reports
`output_route=local_sink_and_fanout`, `result_reuse_for_fanout=true`,
`fanout_result_reuse_hit=true`, per-fanout output formats/paths/digests, workspace path-safety,
certificate, replay, and fidelity fields. Source-free `ctx.sql(...).collect()` remains a
deterministic unsupported diagnostic because the generated-source evidence contract requires an
explicit output sink. The source-free path rejects input datasets, arbitrary `FROM` sources,
unsupported function projections, joins, subqueries, UDFs, object-store paths, table writes, and
broad SQL with deterministic no-fallback errors.

The contract separates three cases:

- `no_dataset_smoke`: status/capability/proof smoke only; no generated rows, no
  source Native I/O certificate, and no output data claim.
- `user_generated_source`: scoped local user rows, literal tables, calendar/date dimensions, and
  generated-row projection/literal `with_column` are supported for JSONL/CSV fixture-smoke writes
  through `ctx.from_rows(...).write(...)`, `ctx.from_rows(...).with_column(...).select(...).write(...)`,
  `ctx.literal_table(...).write(...)`, and `ctx.calendar(...).write(...)`; feature-gated flat scalar
  Parquet/Arrow IPC/Avro/ORC local sinks are available through `write_parquet(...)`,
  `write_arrow_ipc(...)`, `write_avro(...)`, and `write_orc(...)` when the CLI is built with
  `--features universal-format-io`, and feature-gated local Vortex output is available through
  `write_vortex(...)` when the CLI is built with `--features vortex-write`; direct writes and
  `.fanout(...)` route through the public workflow `run` facade, with fanout reusing the computed
  generated rows through the generated-source fanout evidence contract. Broader generated-source
  APIs remain report-only.
- `engine_native_generated_source`: scoped local `range`, `sequence`, and SQL
  `generate_series`/`range` JSONL/CSV fixture smokes are supported through
  `ctx.range(...).write(...)`, `ctx.range(...).filter(...).with_column(...).sort(...).limit(...).write(...)`,
  `ctx.sequence(...).write(...)`, and `ctx.sql("SELECT * FROM generate_series/range(...)").write(...)`;
  direct writes and `.fanout(...)` route through the public workflow `run` facade for generated
  range/sequence and source-free SQL, and the same feature-gated flat scalar structured and Vortex
  sinks are available through the generated-source write helpers.
  Engine-native `values` and deterministic synthetic profiles remain report-only.

Source-free SQL `VALUES` and literal `SELECT` are runtime-supported as local JSONL/CSV fixture
smokes, plus feature-gated flat scalar Parquet/Arrow IPC/Avro/ORC and Vortex local sinks. Broad SQL
execution and broad DataFrame expression execution are not runtime-supported yet.
Current
source-free API admission rows classify:

- `python_ctx_from_rows`, `python_ctx_literal_table`, `python_ctx_calendar`,
  `python_ctx_range`, `python_ctx_sequence`, and `python_generated_source_write`
  as `fixture_smoke_supported` only for scoped local JSONL/CSV and feature-gated flat scalar
  Parquet/Arrow IPC/Avro/ORC/Vortex generated-output smokes with generated-source and output evidence.
  `GeneratedRowsSource.select(...)` and
  `GeneratedRowsSource.with_column(...)` are scoped Python conveniences over the user-row,
  literal-table, and calendar rows before that same write path.
- SQL literal `SELECT`, SQL `VALUES`, and SQL `generate_series`/`range`
  as `fixture_smoke_supported` only for scoped local JSONL/CSV and feature-gated flat scalar
  Parquet/Arrow IPC/Avro/ORC/Vortex source-free generated-output smokes with generated-source and output
  evidence.
- SQL source-free projection and scoped DataFrame literal projection
  as `fixture_smoke_supported` only for scoped local JSONL/CSV and feature-gated flat scalar
  structured/Vortex generated-output smokes with generated-source and output evidence.
- Broad expression-backed DataFrame projection and expression-backed generated `with_column` forms
  remain blocked/report-only with deterministic blocker IDs.

Admission capability discovery separates scoped runtime rows from report-only or blocked rows.
Scoped SQL `VALUES`, literal `SELECT`, `generate_series`/`range`, and local-source SQL ladder rows
carry parser/binder/planner/runtime evidence when they execute. Report-only or blocked admission
rows still do not parse SQL, bind names, plan a query, generate rows, write output, probe object
stores, invoke Foundry, or invoke external engines. Current no-dataset smoke rows report
`input_dataset_count=0`, `source_io_performed=false`,
`generated_source_created=false`, `output_io_performed=false`, and
`generated_source_certificate_status=not_applicable_no_generated_rows`.
Generated-output runtime must report
`input_dataset_count=0`, `source_io_performed=false`,
`generated_source_created=true`, `generated_source_kind`,
`generated_source_schema_digest`, `generated_source_row_count`,
`generated_source_plan_digest`, optional `generated_source_seed`,
`generation_deterministic`, `output_io_performed`,
`output_native_io_certificate_status`, `generated_source_certificate_status`,
`fallback_attempted=false`, `external_engine_invoked=false`, and
`claim_gate_status`. The current user-row, transformed user-row, literal-table, calendar, range,
sequence, SQL `VALUES`, SQL literal `SELECT`, SQL `generate_series`/`range`, scoped SQL range
projection, scoped DataFrame literal projection, and scoped generated DataFrame `with_column` paths
report
`claim_gate_status=fixture_smoke_only` in their scoped local JSONL/CSV lanes and feature-gated flat
scalar Parquet/Arrow IPC/Avro/ORC/Vortex lanes. Default binaries return deterministic blockers for
structured sinks until built with `--features universal-format-io`, and for Vortex until built with
`--features vortex-write`. Vortex generated-output reports include
`vortex_output_runtime_execution`, `vortex_output_reopen_verified`, `vortex_artifact_digest`,
`upstream_vortex_write_called`, and `upstream_vortex_scan_called`.
`ctx.generated_output_to_object_store(...)` now admits a scoped local-emulator fixture route by
staging generated rows through `generated-source-user-rows` and then committing them through
`object-store-write-smoke`; live S3/GCS/ADLS providers, table/lakehouse commits, and production
object-store claims remain gated. `ctx.foundry_generated_output(...)` admits only the local
Foundry-style result/evidence dataset proof; real Foundry output APIs, production Foundry runtime,
and direct S3/object-store shortcuts remain gated.

The scoped DataFrame source-free projection helper lowers literal aliases to the generated-source
local-output command and returns the same `GeneratedSourceWriteReport` as other generated-output
paths:

```python
ctx.dataframe_source_free_projection("lit(1).alias('value')").write("target/generated-df.jsonl")
```

The scoped generated DataFrame `with_column` helper admits a one-row literal column and writes
through the same generated-source local-output command:

```python
(
    ctx.dataframe_generated_with_column("value", "lit(1)")
    .write("target/generated-df-column.jsonl")
)
```

Generated rows can also be written through the scoped local-emulator object-store route:

```python
object_store = ctx.generated_output_to_object_store(
    "target/object-store/generated.jsonl",
    rows=[{"id": 1, "label": "alpha"}],
    allow_overwrite=True,
)

print(object_store.object_store_write_status)
print(object_store.fallback_attempted, object_store.external_engine_invoked)
```

The Foundry helper is similarly scoped to the local dev-stack proof. A local path writes generated
rows through ShardLoom into a result dataset-shaped directory and writes an evidence
dataset-shaped directory through the local Foundry-style output API:

```python
foundry = ctx.foundry_generated_output(
    "target/foundry/result-dataset",
    rows=[{"id": 1, "label": "alpha"}],
    allow_overwrite=True,
)

print(foundry.foundry_style_output_api_invoked)
print(foundry.fallback_attempted, foundry.external_engine_invoked)
```

Remote object-store generated-output targets and real Foundry references still expose deterministic
unsupported reports when called with `check=False`. Those reports do not stage rows, probe
credentials, invoke real Foundry, call an external engine, or attempt fallback:

```python
remote_report = ctx.generated_output_to_object_store("s3://bucket/out.jsonl", check=False)
foundry_report = ctx.foundry_generated_output("foundry://dataset/output")
```

The client also exposes the P7 claim gate closeout report:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
closeout = client.claim_gate_closeout()

print(closeout.claim_gate_status)
print(closeout.release_readiness_status)
print(closeout.allowed_claims)
print(closeout.blocked_claims)
print(closeout.out_of_scope_claims)
print(closeout.no_runtime, closeout.no_fallback, closeout.no_effects)
```

This maps to `shardloom claim-gate-closeout --format json`. It is report-only:
it does not run workloads, publish packages, probe APIs, run benchmarks, invoke
Foundry, or permit external-engine fallback.

For P7.4 compute-engine closeout, the client exposes the report-only compute
capability matrix and operator-family ladder:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
matrix = client.compute_capability_matrix()

for row in matrix.rows:
    print(row.row_id, row.support_status, row.provider_kind, row.blocker_id)

for family in matrix.operator_families:
    print(family.family_id, family.support_status, family.next_evidence)

print(matrix.matrix_status)
print(matrix.claim_grade_status)
print(matrix.no_runtime, matrix.no_fallback, matrix.no_effects)
```

This maps to `shardloom compute-capability-matrix --format json`. It performs
no runtime execution, data reads, writes, benchmark execution, external effects,
external engine invocation, or fallback execution.

The first ShardLoomNative semantic conformance surface is executable, but only
over side-effect-free in-memory fixtures. It records passed, planned, and
blocked semantic dimensions before any broad SQL/DataFrame runtime claims:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
suite = client.semantic_conformance_suite()

print(suite.semantic_profile)
print(suite.suite_status)
print(suite.executed_fixture_count, suite.passed_fixture_count)

for row in suite.rows:
    print(row.row_id, row.fixture_status, row.blocker_id)
```

This maps to `shardloom semantic-conformance-suite --format json`. Current
fixtures cover the supported in-memory semantic dimensions and keep external
oracles, dataset reads, SQL parsing, runtime execution, writes, and fallback
disabled.

Artifact-rich top-level execution result envelopes can be inspected with
`ExecutionResultEnvelopeView` when a command returns a `shardloom.output.v2`
execution envelope:

```python
from shardloom import ExecutionResultEnvelopeView

def inspect_execution_envelope(envelope):
    result = ExecutionResultEnvelopeView(envelope)

    print(result.plan_id)
    print(result.provider_version)
    print(result.result_refs)
    print(result.artifact_refs)
    print(result.inline_artifact_ids)
    print(result.execution_certificate_refs)
    print(result.native_io_certificate_refs)
    print(result.representation_transitions)
    print(result.evidence_completeness_status)
    print([slot.kind for slot in result.incomplete_evidence_slots])
    print(result.fallback_attempted, result.external_engine_invoked)
```

The view is a typed reader over the CLI protocol. It does not execute unsupported
work, create benchmark rows, write outputs, invoke external engines, or convert
report-only surfaces into runtime support.

## Package Build Smoke

The current source package version is owned by `shardloom.__version__` and the shared workspace
version sources. It is a Python client surface over ShardLoom's CLI, with bundled CLI resources in
supported platform wheels.
Bundled platform-wheel readiness can be checked locally without publishing:

```powershell
python -m pip install build
python scripts/release_dry_run_proof.py --rows 64 --iterations 1
```

That proof builds the CLI, stages it under `shardloom/bin/<system-arch>/` in a temporary package
tree, builds a platform-specific wheel/sdist, installs the wheel in a clean environment, and asserts
that `ShardLoomClient().binary_command()` resolves the bundled CLI without `SHARDLOOM_BIN` or
`SHARDLOOM_REPO_ROOT`.

For a client-only wheel smoke without bundled CLI proof:

```powershell
python -m build python
python -m venv $env:TEMP\shardloom-wheel-smoke
$wheel = Get-ChildItem python\dist\shardloom-*.whl | Select-Object -First 1
& $env:TEMP\shardloom-wheel-smoke\Scripts\python -m pip install $wheel.FullName
& $env:TEMP\shardloom-wheel-smoke\Scripts\python -c "import shardloom; print(shardloom.__version__)"
```

Conda packaging should stay split so the pure Python wrapper can remain
`noarch: python` while the Rust CLI binary is built as a platform-specific
package. Local recipe scaffolds live under `packaging/conda/`:

- `shardloom-cli`: compiled Rust `shardloom` binary.
- `shardloom-python`: pure Python wrapper/import surface.
- Optional `shardloom` metapackage: depends on both the wrapper and CLI for a
  one-command install path.

The recipes are not published packages. A release pass must align versions,
replace local sources with tagged source archives and hashes, review license
metadata, build packages in clean Conda environments, and receive explicit
human approval before publication.

Spark, DataFusion, Polars, DuckDB, pandas, and Dask belong only in optional
benchmark environments; they are not ShardLoom runtime dependencies or fallback
engines.

## Live ETL Smoke

The current live ETL surface is intentionally narrow and explicit.
Compatibility-file mode runs `traditional-analytics-run`, which imports CSV,
JSON/JSONL/NDJSON, Parquet, Arrow IPC, Avro, or ORC inputs into temporary local
Vortex files before running the temporary benchmark operator. Native Vortex mode
runs the same provider runtime exposed to release routes as
`vortex-production-runtime-run` from existing `.vortex` inputs. The
low-level `traditional_analytics_vortex_run` helper can also pass an explicit
`cdc_delta_vortex` artifact for the scoped prepared/native CDC overlay row; that
does not imply broad table CDC or transaction support.

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
result = client.live_etl_smoke(
    "selective filter",
    "benchmarks/traditional_analytics/data/fact.csv",
    "benchmarks/traditional_analytics/data/dim.csv",
    input_format="csv",
    workspace="target/shardloom-python-live-etl",
    verify_native_replay=True,
    write_result_vortex=True,
)

print(result.status)
print(result.field("rows_scanned"))
print(result.field("materialization_boundary_reported"))
print(result.field("output_replay_verified"))
print(result.field("combined_output_digest"))
print(result.field("output_replay_native_io_certificate_status"))
print(result.field("computed_result_sink_replay_verified"))
print(result.field("computed_result_sink_native_io_certificate_status"))
print(result.field("runtime_task_graph_executed"))
print(result.field("runtime_execution_certificate_status"))
print(result.field("runtime_memory_reservations_released"))
print(result.fallback.attempted)
```

Resource sizing is automatic by default. ShardLoom derives applied parallelism,
batch rows, and target partition count from the local machine and source
footprint. Pass `memory_gb=` or `max_parallelism=` only when a job or benchmark
needs explicit caps.

`verify_native_replay=True` maps to the CLI `--verify-native-replay` flag. It
keeps the smoke workflow local, re-opens the emitted Vortex artifacts, compares
the replay result with the first execution, and returns workload-scoped evidence
fields such as `workload_constitution_id`, `benchmark_row_ref`,
`coverage_row_ref`, Vortex artifact digests, commit/cleanup status, and replay
Native I/O certificate status. It is only valid for compatibility-file inputs;
existing `.vortex` inputs already use the native Vortex smoke command directly.

`write_result_vortex=True` maps to `--write-result-vortex`. It writes the
computed result envelope to `result.vortex`, re-opens that Vortex artifact,
checks the stored result JSON and materialized-row count, and returns result-sink
digest, schema, replay, Native I/O certificate, and write-timing fields. A
workflow is reported as `workload_certified` only when source replay and computed
result-sink replay both pass.

The same response now includes local runtime closeout fields for the certified
workflow: deterministic task-graph scheduler refs, bounded queue/backpressure
status, cancellation and retry gate status, memory reservation/request/grant/
release counts, fail-before-OOM status, operator spill claim blockers, and the
runtime execution certificate status. These fields remain workload-scoped
evidence for `local_vortex_analytics_v1`, not broad SQL/DataFrame runtime
claims.

For the current compatibility-file universal-I/O path, use the replay helper
when you want to see both parts separately: boundary import into Vortex, then
steady-state native Vortex execution from the emitted artifacts.

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
result = client.live_etl_csv_to_vortex_replay(
    "selective filter",
    "benchmarks/traditional_analytics/data/fact.csv",
    "benchmarks/traditional_analytics/data/dim.csv",
    workspace="target/shardloom-python-live-etl",
)

print(result.csv_import.field("fact_vortex_path"))
print(result.native_vortex.field("source_format") if result.native_vortex else None)
print(result.fallback_attempted)
```

For lower-level local Vortex primitive testing, the wrapper exposes a certified
fixture smoke workflow over the same explicit CLI JSON commands used by the
current CG-2/CG-13/CG-16/CG-19 evidence path:

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
result = client.local_vortex_primitive_smoke(
    "shardloom-vortex/tests/fixtures/local_primitive_struct_five.vortex",
)

print(result.commands)
print(result.all_certified)
print(result.filter_project.field("filter_project_local_execution_rows_projected"))
print(result.fallback_attempted)
```

The Python helper path uses the public `run` facade for explicit local primitive
execution, so envelopes have `command=run` plus
`public_workflow_resolved_internal_command` set to `vortex-run`,
`vortex-count-where`, `vortex-filter`, `vortex-project`, or
`vortex-filter-project`. Count-all, no-argument row-level distinct, scoped source-order tail, and
deterministic row-count sampling map through `vortex-run`; count-where, filter, project, and
filter-project map through their scoped primitive commands. Distinct, tail, and sample use explicit
projection, source-order limit or sample fraction, sample seed or integer `random_state`,
optional positive numeric weight column, row-count or fractional replacement-aware sampling,
`memory_gb`, and `max_parallelism`
payloads where relevant.
The lower `vortex-*` commands remain available for direct diagnostics, tests,
and benchmark evidence. Calls without explicit local primitive execution use
the existing metadata/plan evidence surfaces where the CLI supports them.

The repository smoke script prints command, status, certificate, Native I/O,
materialization, work-metric, evidence-artifact, and no-fallback fields:

```powershell
python scripts\write_ci_version_env.py --format powershell | Invoke-Expression
$env:RUSTUP_TOOLCHAIN = $env:SHARDLOOM_RUST_MSRV_TOOLCHAIN
cargo build -p shardloom-cli --features vortex-local-primitives --bin shardloom

$env:PYTHONPATH = "python\src"
python python\examples\local_vortex_primitives_smoke.py --repo-root .
```

The compatibility-source planning smoke shows the adjacent report-only boundary
for CSV, JSON/JSONL/NDJSON, Parquet, and Arrow IPC inputs before any execution claim.
It plans representative local paths without checking that the files exist,
reading data, writing data, or materializing rows:

```powershell
$env:PYTHONPATH = "python\src"
python python\examples\compatibility_source_smoke.py --repo-root .
```

Override planned sources when you want to inspect your own paths:

```powershell
python python\examples\compatibility_source_smoke.py --repo-root . `
  --source csv=data\fact.csv `
  --source ndjson=data\events.ndjson `
  --source parquet=data\fact.parquet
```

The workflow-readiness smoke pulls together the next no-write boundary: output
target preview, compatibility-output translation planning, staged Vortex
write/commit readiness, table/catalog/object-store/remote-source planning, and
migration/correctness/benchmark evidence status.

```python
from shardloom import ShardLoomClient

client = ShardLoomClient.from_repo()
readiness = client.workflow_readiness_smoke()

print(readiness.plan_names)
print(readiness.all_no_write)
print(readiness.all_report_only_or_planned)
print(readiness.blocked_plan_names)
print(readiness.fallback_attempted)
```

The matching script prints the same surfaces grouped by output/commit,
table/remote, and evidence readiness:

```powershell
$env:PYTHONPATH = "python\src"
python python\examples\workflow_readiness_smoke.py --repo-root .
```

This smoke does not create the staged workspace, write manifests, write Vortex
payloads, open object-store credentials, read remote objects, query catalogs,
materialize rows, or invoke fallback engines. Actual write and commit commands
remain separate explicit CLI calls gated by their readiness signals and feature
flags.

## Quickstart Proof

The quickstart proof script stitches the local user flow together: import and
CLI smoke, capability discovery, lazy source planning, unsupported
explain/estimate diagnostics, compatibility-source planning, workflow
readiness, and optional certified local Vortex primitive execution.

```powershell
$env:PYTHONPATH = "python\src"
python python\examples\quickstart_proof.py --repo-root .
```

To include the currently certified fixture execution path, build the CLI with
the local primitive feature and opt in explicitly:

```powershell
python scripts\write_ci_version_env.py --format powershell | Invoke-Expression
$env:RUSTUP_TOOLCHAIN = $env:SHARDLOOM_RUST_MSRV_TOOLCHAIN
cargo build -p shardloom-cli --features vortex-local-primitives --bin shardloom

$env:PYTHONPATH = "python\src"
python python\examples\quickstart_proof.py --repo-root . --run-local-vortex
```

The optional execution path runs only the checked-in
`local_primitive_struct_five.vortex` fixture through explicit local Vortex
primitive flags. The planning portions remain no-write/no-probe, and the script
exits nonzero if fallback is attempted, planning writes occur, or requested
local primitive evidence is not certified.

Universal I/O is broader than local compatibility files. The current adapter
registry also makes object-store, catalog, effectful, and unstructured queues
visible from Python:

```python
adapters = client.input_adapters()
print(adapters.field("common_structured_adapter_order"))
print(adapters.field("critical_structured_adapter_order"))
print(adapters.field("object_store_adapter_order"))
print(adapters.field("catalog_adapter_order"))
print(adapters.field("database_adapter_order"))
print(adapters.field("parquet_status"))
print(adapters.field("sqlite_status"))

plan = client.input_plan("file://tmp/example.parquet")
print(plan.field("source_kind"))
print(plan.field("capability_status"))
print(plan.field("plan_only"))
```

Common structured inputs are tracked as `native_vortex`, `parquet`,
`arrow_ipc`, `csv`, JSON/NDJSON through `jsonl`, `avro`, and `orc`.
Database adapters are visible separately: SQLite has a local import/export
fixture smoke, while Postgres/MySQL, JDBC/ODBC, Snowflake, BigQuery, and
Databricks SQL remain credential/network-gated. Lakehouse/table, object-store,
catalog, effectful, and unstructured/media families are also represented in the
registry. The current implemented live paths are scoped local fixture/evidence
paths only: feature-gated local compatibility-file-to-Vortex benchmark smokes,
native `.vortex` replay, public/local object-store fixture smokes, local table
commit rehearsal, local SQLite import/export smoke, and the built-in
deterministic scalar UDF fixture. Production adapter certification, live
object-store runtime, catalogs, broad SQL/DataFrame runtime, arbitrary UDFs, and
network connectors remain future work.

For a single source/sink compatibility view, use the typed scoreboard instead
of scraping architecture prose:

```python
matrix = ctx.compatibility_scoreboard()
print(matrix.schema_version)
print(matrix.row("vortex").support_status)
print(matrix.row("object_store_s3_gcs_adls").support_status)
print(matrix.all_rows_no_fallback_no_external_engine)

object_store = matrix.object_store_admission_ladder
print(object_store.schema_version)
print(object_store.provider_scope)
print(object_store.runtime_supported)
for row in object_store.rows:
    print(
        row.row_id,
        row.support_status,
        row.credential_policy_status,
        row.no_effects_no_fallback,
    )
```

The scoreboard maps local files, Vortex, generated outputs, Python rows,
SQL literals, databases, object stores, table/lakehouse formats, remote APIs,
and Foundry to `runtime-supported`, `smoke-supported`, `report-only`,
`blocked`, or `not-planned`. It is a capability map only, not a production,
performance, SQL/DataFrame, object-store/lakehouse, Foundry, or package claim.
The `object_store_admission_ladder` keeps S3/GCS/ADLS URI recognition,
credential policy, public reads, authenticated reads, byte-range reads,
full-file reads, local cache, write staging, and commit protocol as separate
gates. Current rows keep credential resolution, provider probes, network
probes, object-store I/O, writes, commits, external engines, and fallback
disabled.
Important row IDs include `object_store_uri_parse`, `credential_policy`,
`public_no_credential_read`, `authenticated_read`, `byte_range_read`,
`full_file_read`, `local_cache`, `write_staging`, and `commit_protocol`.

For the first explicit object-store read runtime proof, use the local-emulator
smoke. It reads a local fixture file through an object-store-style profile and
emits SourceState, byte-range/full-file read, Native I/O, and no-fallback
evidence.

```python
read = client.object_store_read_smoke(
    "target/object-store-fixture.bin",
    byte_range=(0, 16),
)
print(read.field("object_store_read_status"))
print(read.field("source_state_id"))
print(read.field_bool("network_probe_performed"))
print(read.field_bool("fallback_attempted"))
```

For the public no-credential fixture profile, pass a supported S3/GCS/ADLS URI
and an explicit local fixture file. ShardLoom parses the provider URI and reads
the fixture bytes only; it does not resolve credentials, probe the provider, or
open a network connection.

```python
public_read = client.object_store_read_smoke(
    "s3://shardloom-public-fixtures/orders.vortex",
    profile="public-no-credential-fixture",
    public_fixture_path="target/object-store-public-fixture.vortex",
    fixture_listing=True,
    byte_range=(0, 16),
)
print(public_read.field("object_store_uri_parse_status"))
print(public_read.field("native_io_certificate_status"))
print(public_read.field_bool("public_no_credential_fixture_claim_allowed"))
print(public_read.field_bool("network_probe_performed"))
```

For the first explicit object-store write runtime proof, use the separate
local-emulator write smoke. It stages a local source file into a local-emulator
target path, commits a sidecar manifest, emits idempotency and digest evidence,
and can immediately roll back the object plus manifest for cleanup proof.

```python
write = client.object_store_write_smoke(
    "target/source.bin",
    "target/object-store-fixture.bin",
    idempotency_key="orders-batch-001",
    rollback_after_commit=True,
)
print(write.field("object_store_write_status"))
print(write.field("commit_protocol_status"))
print(write.field("rollback_status"))
print(write.field_bool("object_store_write_io"))
print(write.field_bool("fallback_attempted"))
```

Object-store read/write smokes remain fixture-scoped. Live real S3/GCS/ADLS
network reads, credentials, provider probes, signed URLs, authenticated cloud
reads or writes, cache writes, table/lakehouse commits, catalog interaction,
distributed runtime, and production object-store claims remain blocked.

For the local SQLite adapter fixture, create or point at a local SQLite file and
use the import/export smoke. The command table-scans a named table, writes a
workspace-safe JSONL export, and creates a roundtrip SQLite artifact. It does not
accept arbitrary SQL, push queries down, connect to network databases, resolve
credentials, load extensions, or use SQLite as a fallback engine. `order_by` is
post-scan fixture ordering in ShardLoom, and BLOB schemas/values are rejected.

```python
sqlite = client.sqlite_local_import_export_smoke(
    "target/orders.sqlite",
    table="orders",
    export_jsonl="target/orders-sqlite.jsonl",
    roundtrip_db="target/orders-roundtrip.sqlite",
    order_by="id",
    allow_overwrite=True,
)
print(sqlite.field("sqlite_sql_execution_scope"))
print(sqlite.field_bool("sqlite_query_pushdown_allowed"))
print(sqlite.field("sqlite_ordering_execution_scope"))
print(sqlite.field_bool("roundtrip_replay_verified"))
```

For the built-in deterministic scalar UDF fixture, use the nullable-int64
fixture smoke. It proves UDF metadata, determinism, null propagation, overflow
blocking, and effect policy for one built-in fixture only. It is not Python,
WASM, Rust plugin, SQL-defined, table-function, or external-service UDF support.

```python
registry = client.udf_registry()
print(registry.field("typed_udf_registry_support_status"))
print(registry.field_int("typed_udf_registry_admitted_local_fixture_count"))
print(registry.field_bool("typed_udf_registry_arbitrary_runtime_bridge_available"))

udf = client.udf_local_scalar_fixture_smoke([1, None, 3])
print(udf.field("udf_id"))
print(udf.field("output_values"))
print(udf.field_bool("external_effect_executed"))
print(udf.field_bool("fallback_attempted"))
```

Extension metadata and UDF runtime posture remain inspectable without executing
extension code. A local extension manifest can be inspected as bounded metadata;
the CLI does not load extension code, resolve credentials, probe networks, or
enable plugin runtime support. The same helpers are available on
`ShardLoomContext` when you want one high-level workflow surface:

```python
extensions = client.extension_registry()
extension_dir = client.extension_registry(manifest_dir="target/extensions")
manifest = client.extension_inspect(manifest_path="target/extension.json")
typed_udfs = client.udf_registry()
fixture_plan = client.udf_runtime_plan("fixture")
python_plan = client.udf_runtime_plan("python")
print(extensions.field("extension_manifest_effect_all_runtime_blocked"))
print(extension_dir.field("extension_registry_manifest_count"))
print(extension_dir.field_bool("extension_registry_extension_code_executed"))
print(manifest.field("extension_manifest_inspection_status"))
print(manifest.field_bool("extension_manifest_execution_contract_complete"))
print(manifest.field_bool("extension_manifest_extension_code_executed"))
print(typed_udfs.field("typed_udf_registry_row_order"))
print(typed_udfs.field_bool("typed_udf_registry_external_engine_invoked"))
print(fixture_plan.field("udf_runtime_kind"))
print(python_plan.field_bool("udf_runtime_sandboxing_required"))

ctx_extensions = ctx.extension_registry()
ctx_extension_dir = ctx.extension_registry(manifest_dir="target/extensions")
ctx_manifest = ctx.extension_inspect(manifest_path="target/extension.json")
ctx_typed_udfs = ctx.udf_registry()
ctx_udf = ctx.udf_local_scalar_fixture_smoke([1, None, 3])
print(ctx_extensions.field_bool("extension_code_executed"))
print(ctx_extension_dir.field_bool("extension_registry_runtime_execution"))
print(ctx_manifest.field_bool("extension_manifest_external_effect_executed"))
print(ctx_typed_udfs.field_bool("typed_udf_registry_fallback_attempted"))
print(ctx_udf.field_bool("fallback_attempted"))
```

For the scoped local table metadata read proof, use the local-manifest smoke.
It emits a typed metadata summary and digest evidence from ShardLoom's local
manifest fixture without reading data files, touching object stores, resolving
credentials, invoking table-format dependencies, or using fallback engines.

```python
metadata = client.local_table_metadata_read_smoke()
print(metadata.field("support_status"))
print(metadata.field("claim_gate_status"))
print(metadata.field_bool("table_metadata_read_performed"))
print(metadata.field_bool("object_store_io_performed"))
print(metadata.field_bool("fallback_attempted"))
```

For the first fixture-scoped table append commit rehearsal, use the local
manifest smoke. It writes a staged committed manifest plus sidecar table commit
record, reports base/append/committed snapshot ids and digest evidence, and can
immediately roll both artifacts back for cleanup proof.

```python
table = client.local_table_append_commit_rehearsal_smoke(
    "target/table-commit/metadata-v2.json",
    idempotency_key="orders-table-commit-001",
    rollback_after_commit=True,
)
print(table.field("table_append_commit_status"))
print(table.field("committed_snapshot_id"))
print(table.field("commit_protocol_status"))
print(table.field_bool("table_catalog_commit_performed"))
print(table.field_bool("object_store_io"))
print(table.field_bool("fallback_attempted"))
```

The table metadata and append-commit smokes are `local-manifest` fixtures only.
They are not Iceberg/Delta/Hudi production metadata/runtime support, catalog
transactions, object-store-backed table commits, merge/update/delete runtime,
distributed runtime, or performance claims.

The same scoreboard exposes table-format boundaries:

```python
tables = matrix.table_format_boundary_matrix
print(tables.schema_version)
print(tables.format_scope)
print(tables.local_metadata_smoke_available)
print(tables.runtime_supported)
for row in tables.rows:
    print(row.row_id, row.support_status, row.no_io_no_fallback)
```

The `table_format_boundary_matrix` keeps Iceberg, Delta, and Hudi metadata
reads, table scans, snapshot/time-travel, partition evolution, delete/tombstone,
append, merge/update/delete, commit, rollback, catalog interaction, and
object-store coupling as separate gates. Local manifest metadata, delete/
tombstone, and append commit rehearsal smokes are related evidence only; they
are not production table-format runtime, lakehouse runtime, catalog runtime,
object-store runtime, or commit support.
Important row IDs include `table_metadata_read`, `table_scan`,
`snapshot_time_travel`, `partition_evolution`, `delete_tombstone`, `append`,
`merge_update_delete`, `commit`, `rollback`, `catalog_interaction`, and
`object_store_coupling`.

The same scoreboard exposes database and warehouse import/export boundaries:

```python
endpoints = matrix.database_warehouse_boundary_matrix
print(endpoints.schema_version)
print(endpoints.endpoint_scope)
print(endpoints.runtime_supported)
for row in endpoints.rows:
    print(
        row.row_id,
        row.support_status,
        row.credential_required,
        row.network_required,
        row.no_effects_no_fallback,
    )
```

The `database_warehouse_boundary_matrix` keeps SQLite, Postgres, MySQL,
JDBC/ODBC, Snowflake, BigQuery, and Databricks SQL separate. SQLite is the only
admitted fixture exception: `sqlite_file` is smoke-supported for local named
table import/export through `sqlite-local-import-export-smoke`, with query
pushdown disabled and no credentials/network probes. Postgres/MySQL, JDBC/ODBC,
Snowflake, BigQuery, and Databricks SQL remain blocked as connectors and cannot
serve as fallback engines. Important row IDs include `sqlite_file`, `postgres`,
`mysql`, `jdbc_odbc`, `snowflake`, `bigquery`, and `databricks_sql`.

The client also exposes advisory optimization reports:

```python
dynamic = client.dynamic_work_shaping_plan("memory-pressure")
sizing = client.sizing_feedback_plan(8, ["task-too-large", "memory-pressure-high"])
```

These commands report planned/advisory state only; they do not mutate runtime
policy yet.

Planning and evidence commands may return `status="success"` while including
error-severity diagnostics that describe missing evidence or blocked future
work. The Python client preserves those diagnostics for inspection instead of
raising unless the CLI exits nonzero or the envelope status is `error` or
`unsupported`.

The example script wires the same calls together:

```powershell
$env:PYTHONPATH = "python\src"
python python\examples\live_etl_smoke.py `
  --mode csv `
  --scenario "selective filter" `
  --fact benchmarks\traditional_analytics\data\fact.csv `
  --dim benchmarks\traditional_analytics\data\dim.csv `
  --workspace target\shardloom-python-live-etl
```

## Test

```powershell
$env:PYTHONPATH = "python\src"
python -m unittest discover python\tests
```
