Metadata-Version: 2.4
Name: commonground-scenarios
Version: 0.1.0
Summary: Deterministic planted-scenario generation for Common Ground environments.
Author: Common Ground contributors
License-Expression: Apache-2.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.12, <3.13
Project-URL: Repository, https://github.com/<OWNER>/commonground-envs
Description-Content-Type: text/markdown

# commonground-scenarios

Deterministic, offline scenario generation for the Common Ground environment
family. Each generated scenario contains a fictional organization, policy
documents, planted ambiguities/contradictions/gaps, precise distractors, a
seeded persona panel, and provenance.

Every canonical clarifying question is phrased as a yes/no proposition. Its
`target_stances` have an explicit interpretation: `agree` predicts yes,
`disagree` predicts no, and `pass` means the faction takes no position.
Templates may also author a finite `canonical_question_aliases` allowlist.
Aliases obey the same yes/no form and are kept distinct across the answer key.

The default generator uses committed templates only. An operator may inject a
prose-polishing callback, but no model client or network path is built into the
package, and validation requires every planted anchor to survive polishing.

Training and held-out template registries are disjoint. Generated scenarios are
canonical JSON and can be regenerated byte-for-byte from the same template,
seed, and explicit generation date.
