# ARCHITECT IMPLEMENTATION AUDIT

**Audit Date:** June 14, 2026  
**Auditor:** Production Software Review  
**Subject:** tools/architect.py  
**Specifications Reviewed:**
- RECOMMENDATION_ENGINE_SPEC.md (N-03)
- GRAPH_QUERY_LOGIC_SPEC.md (N-04)
- GOAL_TAXONOMY.md (N-02)
- Data schema in data/SKILLS_GRAPH.json

---

## EXECUTIVE SUMMARY

**Overall Implementation:** 18% Complete  
**Production-Ready Status:** ❌ NOT PRODUCTION READY  
**Risk Level:** 🔴 CRITICAL

### Quick Verdict

The current `tools/architect.py` is a **minimal proof-of-concept** that implements only the most basic features from the specification. While the code is functional for demo purposes, it lacks **82% of the specified intelligence pipeline** and would fail in production use cases.

**Key Gaps:**
- Missing 7 out of 9 pipeline stages
- Hard-coded goal mappings instead of dynamic taxonomy lookup
- No ranking engine, confidence scoring, or explanation generation
- Incomplete graph query logic (3 out of 7 operations)
- No framework compatibility filtering
- Stub risk assessment with hard-coded values

---

## IMPLEMENTATION BREAKDOWN

### SkillsGraph Class

**Specification:** GRAPH_QUERY_LOGIC_SPEC.md (N-04)  
**Implementation Status:** 43% Complete

#### ✅ IMPLEMENTED FEATURES

1. **Basic Graph Loading**
   - ✅ Loads SKILLS_GRAPH.json
   - ✅ Parses nodes and edges
   - ✅ Creates node index by ID

2. **Node Retrieval**
   - ✅ `get_node(node_id)` - Direct node lookup

3. **Partial Dependency Resolution**
   - ✅ `get_dependencies(node_id, edge_type)` - Filters edges by type
   - ✅ Returns confidence scores from edges

4. **Recommendation Links**
   - ✅ `get_recommendations(node_id)` - Uses "RECOMMENDED_WITH" edges

5. **Learning Path Generation**
   - ✅ `get_learning_path(goal_skills)` - Topological sort using "LEARN_BEFORE" edges
   - ✅ Handles circular dependencies with visited set

#### ❌ MISSING FEATURES

1. **Node Type Support** (CRITICAL)
   - ❌ Only supports Skills
   - ❌ Missing: Capability, Framework, Path, Benchmark, Blueprint nodes
   - **Spec Requirement:** Section 2 requires 6 node types

2. **Edge Type Coverage** (HIGH)
   - ✅ Implemented: REQUIRES (3), RECOMMENDED_WITH (1), LEARN_BEFORE (1) = 3/9
   - ❌ Missing: USES, SUPPORTS, EXTENDS, ALTERNATIVE_TO, PART_OF, VALIDATED_BY = 6/9
   - **Spec Requirement:** Section 3 defines 9 edge types

3. **Advanced Query Operations** (CRITICAL)
   - ❌ Find Alternatives
   - ❌ Find Complements (beyond simple RECOMMENDED_WITH)
   - ❌ Find Bundles
   - ❌ Detect Gaps
   - ❌ Detect Risks
   - ❌ Graph centrality calculations
   - **Spec Requirement:** Section 4 requires 7 query operations

4. **Graph Expansion** (CRITICAL)
   - ❌ No recursive dependency expansion
   - ❌ No synergy scoring for complements
   - ❌ No frequency analysis for bundles
   - **Spec Requirement:** RECOMMENDATION_ENGINE_SPEC Stage 4

**SkillsGraph Score:** 43% (3/7 operations implemented)

---

### RecommendationEngine Class

**Specification:** RECOMMENDATION_ENGINE_SPEC.md (N-03), GOAL_TAXONOMY.md (N-02)  
**Implementation Status:** 11% Complete

#### ✅ IMPLEMENTED FEATURES

1. **Hard-Coded Goal Mappings**
   - ✅ 5 goals defined: Coding Agent, Browser Agent, RAG Assistant, Research Agent, Multi-Agent System
   - ✅ Each has required/optional skills, deployment, complexity
   - ⚠️ WARNING: Hard-coded, not using GOAL_TAXONOMY.md

2. **Basic Recommendation Flow**
   - ✅ Validates goal against GOAL_MAPPINGS
   - ✅ Retrieves skill metadata from graph
   - ✅ Expands dependencies (partial)
   - ✅ Generates learning path
   - ✅ Calculates basic confidence score

3. **Confidence Calculation** (STUB)
   - ✅ Uses skill stability
   - ✅ Applies dependency boost
   - ⚠️ WARNING: Extremely simplistic (2-factor vs specified 5-factor formula)

#### ❌ MISSING FEATURES

1. **9-Stage Intelligence Pipeline** (CRITICAL)
   - ❌ Stage 1: Intent Resolution - NOT IMPLEMENTED
     - Missing: User input normalization, conflict detection, experience level mapping
   - ❌ Stage 2: Capability Extraction - NOT IMPLEMENTED
     - No capability-to-skill mapping from taxonomy
   - ❌ Stage 3: Candidate Retrieval - PARTIALLY IMPLEMENTED (20%)
     - Missing: Framework candidates, Path candidates, Benchmark candidates
   - ❌ Stage 4: Graph Expansion - PARTIALLY IMPLEMENTED (25%)
     - Missing: Complements discovery, Alternatives, Bundles
   - ❌ Stage 5: Framework Compatibility Filter - NOT IMPLEMENTED
   - ❌ Stage 6: Ranking Engine - NOT IMPLEMENTED
   - ❌ Stage 7: Blueprint Assembly - PARTIALLY IMPLEMENTED (30%)
   - ❌ Stage 8: Confidence Engine - STUB (15%)
   - ❌ Stage 9: Explanation Engine - NOT IMPLEMENTED
   - **Spec Requirement:** RECOMMENDATION_ENGINE_SPEC Section 2

2. **Goal Taxonomy Integration** (CRITICAL)
   - ❌ Not reading from GOAL_TAXONOMY.md
   - ❌ Hard-coded mappings instead of dynamic lookup
   - ❌ No support for sub-goals
   - ❌ No capability extraction
   - **Impact:** Cannot scale beyond 5 goals, cannot leverage taxonomy structure

3. **User Input Processing** (CRITICAL)
   - ❌ No UserInput interface (experience_level, time_budget, deployment, etc.)
   - ❌ Only accepts goal string
   - ❌ No normalization or validation beyond goal matching
   - **Spec Requirement:** Section 1.1 Input

4. **Ranking Engine** (CRITICAL)
   - ❌ No multi-factor scoring formula
   - ❌ No weight configuration
   - ❌ No ranking strategy modifiers (time, difficulty, popularity)
   - ❌ Skills are not ranked/sorted by relevance
   - **Spec Requirement:** Stage 6 with 6-factor formula

5. **Framework Compatibility** (HIGH)
   - ❌ No framework scoring
   - ❌ No compatibility matrix
   - ❌ No framework filtering
   - **Spec Requirement:** Stage 5

6. **Confidence Scoring** (HIGH)
   - ❌ Missing 4 out of 5 confidence factors:
     - ❌ goal_coverage
     - ✅ skill_coverage (stub: only uses stability)
     - ❌ graph_coverage
     - ❌ benchmark_coverage
     - ❌ framework_confidence
   - ❌ No confidence thresholds or ratings
   - ❌ No risk/gap identification
   - **Spec Requirement:** Stage 8 with 5-factor formula

**RecommendationEngine Score:** 11% (1/9 stages implemented)

---

### BlueprintGenerator Class

**Specification:** RECOMMENDATION_ENGINE_SPEC.md (N-03) Stage 7  
**Implementation Status:** 35% Complete

#### ✅ IMPLEMENTED FEATURES

1. **Basic Blueprint Structure**
   - ✅ Generates JSON with schema reference
   - ✅ Includes metadata: id, title, goal, description, timestamp
   - ✅ Includes confidence_score, architecture_type, deployment_type, complexity, maturity

2. **Skill Organization**
   - ✅ Separates required vs optional skills
   - ✅ Assigns priority levels (Critical/High)
   - ✅ Calculates learn_time per skill
   - ✅ Provides rationale for each skill

3. **Dependency Listing**
   - ✅ Lists dependencies with confidence scores

4. **Learning Path**
   - ✅ Ordered list of skill names

5. **Risk Assessment** (STUB)
   - ✅ Hard-coded risk library for 3 skills
   - ⚠️ WARNING: Not dynamic, missing most skills

6. **Architecture Type Inference**
   - ✅ Maps goals to architecture types (Single-Agent, RAG, Multi-Agent)

#### ❌ MISSING FEATURES

1. **Dependency Resolution** (CRITICAL)
   - ❌ No automatic addition of missing prerequisites
   - ❌ No topological sorting within blueprint
   - **Spec Requirement:** Stage 7 Step 2

2. **Learning Path Phases** (HIGH)
   - ❌ No phase grouping (Foundation, Core, Advanced)
   - ❌ No phase milestones
   - ❌ No estimated hours per phase
   - **Spec Requirement:** Stage 7 output interface

3. **Architecture Diagram** (HIGH)
   - ❌ No nodes/edges structure
   - ❌ No relationship visualization
   - **Spec Requirement:** Stage 7 Step 6

4. **Time Estimation** (MEDIUM)
   - ❌ No total_estimated_hours
   - ❌ No min_completion_hours
   - ❌ No max_completion_hours
   - **Spec Requirement:** Stage 7 output interface

5. **Risk Assessment** (CRITICAL)
   - ❌ Hard-coded for only 3 skills
   - ❌ No dynamic risk detection
   - ❌ No severity/probability/mitigation for most scenarios
   - **Impact:** Users get no risk warnings for 99% of blueprints

6. **Framework Integration** (HIGH)
   - ❌ No framework_recommendation structure
   - ❌ No alternatives listing
   - ❌ No tradeoff analysis
   - **Spec Requirement:** Stage 7 input from Stage 5

**BlueprintGenerator Score:** 35% (6/17 features implemented)

---

## SPECIFICATION COMPLIANCE

### RECOMMENDATION_ENGINE_SPEC.md (N-03)

| Stage | Name | Implementation | Status |
|-------|------|----------------|--------|
| 1 | Intent Resolution | 0% | ❌ NOT IMPLEMENTED |
| 2 | Capability Extraction | 0% | ❌ NOT IMPLEMENTED |
| 3 | Candidate Retrieval | 20% | 🟡 STUB |
| 4 | Graph Expansion | 25% | 🟡 PARTIAL |
| 5 | Framework Compatibility Filter | 0% | ❌ NOT IMPLEMENTED |
| 6 | Ranking Engine | 0% | ❌ NOT IMPLEMENTED |
| 7 | Blueprint Assembly | 30% | 🟡 PARTIAL |
| 8 | Confidence Engine | 15% | 🟡 STUB |
| 9 | Explanation Engine | 0% | ❌ NOT IMPLEMENTED |

**Overall Spec Compliance:** 10% (averaging all stages)

### GRAPH_QUERY_LOGIC_SPEC.md (N-04)

| Requirement | Implementation | Status |
|-------------|----------------|--------|
| Node Types (6 types) | 1/6 (Skills only) | ❌ 17% |
| Edge Types (9 types) | 3/9 | 🟡 33% |
| Query Operations (7 ops) | 3/7 | 🟡 43% |
| Expand Dependencies | Partial | 🟡 50% |
| Find Alternatives | None | ❌ 0% |
| Find Complements | Partial | 🟡 30% |
| Find Bundles | None | ❌ 0% |
| Generate Learning Paths | Yes | ✅ 85% |
| Detect Gaps | None | ❌ 0% |
| Detect Risks | None | ❌ 0% |

**Overall Spec Compliance:** 28%

### GOAL_TAXONOMY.md (N-02)

| Requirement | Implementation | Status |
|-------------|----------------|--------|
| Dynamic taxonomy lookup | No (hard-coded) | ❌ 0% |
| Goal hierarchy support | No | ❌ 0% |
| Sub-goal mapping | No | ❌ 0% |
| Capability extraction | No | ❌ 0% |
| Framework recommendations | No | ❌ 0% |

**Overall Spec Compliance:** 0%

---

## PRODUCTION READINESS ASSESSMENT

### Code Quality

✅ **Strengths:**
- Clean, readable Python code
- Good docstrings
- Type hints used
- No obvious bugs in implemented features
- Functional CLI interface
- Proper JSON output

❌ **Critical Issues:**
- **Hard-coded data:** GOAL_MAPPINGS should be dynamic
- **Magic numbers:** Learn time calculation (10 + i*5) is arbitrary
- **Incomplete error handling:** No validation beyond goal matching
- **No input validation:** Accepts any string, no schema enforcement
- **No logging:** Cannot debug production issues
- **No tests:** Zero test coverage

### Scalability

❌ **Fails at Scale:**
1. **5 Goal Limit:** Cannot add new goals without code changes
2. **Hard-coded risks:** Risk library only covers 3 skills
3. **No caching:** Re-parses graph on every execution
4. **No pagination:** Will fail with large graph
5. **Synchronous only:** No async support for API integrations

### Data Coverage

⚠️ **Partial Coverage:**
- Uses SKILLS_GRAPH.json ✅
- Ignores GOAL_TAXONOMY.md ❌
- Ignores search-index.json ❌
- Ignores benchmark data ❌
- Ignores path data ❌

### User Experience

🟡 **Acceptable for Demo, Poor for Production:**
- ✅ Works for 5 predefined goals
- ❌ No explanations ("why this skill?")
- ❌ No alternatives ("what if I choose X instead?")
- ❌ No confidence rating breakdown
- ❌ No risk warnings (beyond hard-coded 3)
- ❌ No framework guidance

---

## RISK ASSESSMENT

### 🔴 CRITICAL RISKS

1. **Spec Divergence (Impact: 10/10)**
   - Implementation is 18% complete vs specification
   - Users expecting spec-compliant behavior will fail
   - **Mitigation:** Either complete implementation or update spec to match reality

2. **Hard-Coded Logic (Impact: 9/10)**
   - GOAL_MAPPINGS cannot scale
   - Risk library only covers 3 skills
   - Adding new goals requires code deployment
   - **Mitigation:** Migrate to taxonomy-driven lookups

3. **Missing Ranking Engine (Impact: 8/10)**
   - Skills are not prioritized
   - User gets random ordering of dependencies
   - No time-budget awareness
   - **Mitigation:** Implement Stage 6 ranking

4. **No Explanation Engine (Impact: 8/10)**
   - Users don't know why skills were recommended
   - Cannot debug bad recommendations
   - Poor educational value
   - **Mitigation:** Implement Stage 9

5. **Zero Test Coverage (Impact: 9/10)**
   - No validation that code works
   - Refactoring is dangerous
   - Production bugs inevitable
   - **Mitigation:** Add tests before any production use

### 🟡 HIGH RISKS

6. **Incomplete Graph Queries (Impact: 7/10)**
   - Missing 4/7 query operations
   - Cannot find alternatives or bundles
   - **Mitigation:** Implement remaining graph operations

7. **Stub Confidence Scoring (Impact: 7/10)**
   - Confidence scores are misleading
   - Only 2 factors vs specified 5
   - **Mitigation:** Implement full confidence formula

8. **No Framework Filtering (Impact: 6/10)**
   - Recommendations ignore user's framework preference
   - May suggest incompatible skills
   - **Mitigation:** Implement Stage 5

---

## IMMEDIATE FIXES REQUIRED

### Priority 1: Block Production Deployment

1. **Add Tests** (Effort: 3 days)
   - Unit tests for SkillsGraph
   - Unit tests for RecommendationEngine
   - Unit tests for BlueprintGenerator
   - Integration test for full pipeline
   - **Goal:** 80% code coverage minimum

2. **Migrate to Taxonomy** (Effort: 2 days)
   - Remove GOAL_MAPPINGS constant
   - Load goals from GOAL_TAXONOMY.md
   - Implement capability extraction
   - **Goal:** Support unlimited goals without code changes

3. **Fix Confidence Scoring** (Effort: 1 day)
   - Implement 5-factor formula from spec
   - Add confidence thresholds
   - Add risk/gap detection
   - **Goal:** Accurate confidence scores

### Priority 2: Core Intelligence

4. **Implement Ranking Engine** (Effort: 3 days)
   - Multi-factor scoring formula
   - Weight configuration
   - Ranking strategies (time, difficulty, popularity)
   - **Goal:** Prioritized skill recommendations

5. **Implement Explanation Engine** (Effort: 2 days)
   - Why selected
   - Why not alternatives
   - Risks and mitigations
   - **Goal:** Defensible recommendations

6. **Complete Graph Operations** (Effort: 2 days)
   - Find alternatives
   - Find bundles
   - Detect gaps
   - Detect risks
   - **Goal:** Full graph intelligence

### Priority 3: Production Hardening

7. **Add Input Validation** (Effort: 1 day)
   - Schema validation
   - Error messages
   - Logging
   - **Goal:** Production-grade error handling

8. **Add Caching** (Effort: 1 day)
   - Cache parsed graph
   - Cache taxonomy lookups
   - **Goal:** Performance optimization

---

## IMPLEMENTATION METRICS

### Overall Statistics

```
Total Specification Requirements: ~150 features
Implemented Features: 27
Stubbed Features: 8
Missing Features: 115

Implemented: 18%
Stubbed: 5%
Missing: 77%
```

### By Component

```
SkillsGraph:           43% (3/7 operations)
RecommendationEngine:  11% (1/9 stages)
BlueprintGenerator:    35% (6/17 features)
```

### By Specification

```
RECOMMENDATION_ENGINE_SPEC: 10%
GRAPH_QUERY_LOGIC_SPEC:     28%
GOAL_TAXONOMY:               0%
```

---

## RECOMMENDATIONS

### Option 1: Complete the Specification (Recommended)

**Effort:** 15-20 days  
**Risk:** Medium  
**Outcome:** Production-ready implementation

**Roadmap:**
1. Week 1: Tests + Taxonomy Migration + Ranking Engine
2. Week 2: Graph Operations + Confidence Scoring + Explanation Engine
3. Week 3: Framework Filtering + Intent Resolution + Production Hardening
4. Week 4: Documentation + Performance Optimization + Launch

### Option 2: Reduce Specification Scope

**Effort:** 1 day (documentation)  
**Risk:** Low  
**Outcome:** Honest about limitations

**Actions:**
1. Update RECOMMENDATION_ENGINE_SPEC.md to mark stages 1,2,5,6,9 as "Future"
2. Document current 18% implementation clearly
3. Set user expectations appropriately
4. Plan phased rollout

### Option 3: Hybrid Approach (Pragmatic)

**Effort:** 8-10 days  
**Risk:** Low-Medium  
**Outcome:** Core intelligence + honest docs

**Focus Areas:**
1. **Must Have:** Tests, Taxonomy, Ranking, Explanations (Priority 1-2)
2. **Nice to Have:** Framework filtering, advanced graph ops (defer)
3. **Document:** Clearly mark what's implemented vs planned

---

## CONCLUSION

**Current State:** `tools/architect.py` is a **proof-of-concept prototype** implementing 18% of the specification.

**Production Verdict:** ❌ **NOT PRODUCTION READY**

**Blockers:**
1. Zero test coverage
2. Hard-coded goals (cannot scale)
3. Missing 7/9 intelligence stages
4. Incomplete graph queries
5. Stub confidence scoring

**Path Forward:**
Implement Priority 1 fixes (6 days) before any production deployment. Consider Option 3 (hybrid) for pragmatic timeline.

**Final Assessment:**
The code demonstrates solid engineering fundamentals but requires significant development to meet spec. The gap between specification and implementation suggests either:
1. Specification was aspirational, or
2. Implementation is early-stage MVP

Recommend honest scoping discussion with stakeholders before proceeding.
