2 Commits

Author	SHA1	Message	Date
freemo	f2232eec09	fix(acms): align SkeletonCompressorService.compress() with SkeletonCompressor protocol ... - Updated SkeletonCompressorService.compress() to accept (fragments: tuple[ContextFragment, ...], skeleton_budget: int) -> tuple[ContextFragment, ...], matching the SkeletonCompressor protocol - Removed skeleton_ratio and CompressionResult from the public API - Added @runtime_checkable to SkeletonCompressor protocol in acms_service.py - Added structural subtype assertion at module level to prevent future protocol drift - Rewrote all Behave feature scenarios and step definitions to use skeleton_budget - Updated benchmarks and robot helpers to use absolute token budgets - Removed CompressionResult export from services __init__.py and vulture_whitelist.py - The depth_breadth_projection.py call site already correctly computed and passed skeleton_budget ISSUES CLOSED: #2925	2026-05-30 08:29:22 -04:00
freemo	711e867112	feat(acms): add skeleton compressor ... Implemented SkeletonCompressorService for ACMS context inheritance, producing compressed context representations for propagation from parent plans to child plans. Key design decisions and implementation details: - SkeletonMetadata (frozen Pydantic model): records ratio, original_tokens, compressed_tokens, and source_decision_ids for full auditability of each compression pass. Persisted on Plan.skeleton_metadata. - SkeletonCompressorService: stateless service accepting a list of ContextFragment objects and a skeleton_ratio in [0.0, 1.0]. Fragments are sorted by relevance descending with a stable secondary sort on fragment_id to guarantee deterministic output. Token budget is original_tokens*(1-ratio); fragments are greedily selected until budget is exhausted. - Ratio semantics: 0.0 = no compression (pass-through), 1.0 = maximum compression (single top fragment only), None = default 0.3. - Integration: Plan model gains optional skeleton_metadata field exposed in as_cli_dict() under the 'skeleton' key. Service registered in DI container as skeleton_compressor_service (Singleton, stateless). - Tests: 22 BDD scenarios (features/skeleton_compressor.feature) covering ratio validation, stable ordering, metadata correctness, edge cases, and plan model integration. 6 Robot Framework smoke tests. ASV benchmark suites at 10/100/1000 fragment scales. - Documentation: docs/reference/skeleton_compressor.md with ratio table, algorithm description, metadata schema, and multi-decision plan example. ISSUES CLOSED: #194	2026-03-03 03:36:33 +00:00