# Phase 165.4-04 Summary

Status: implemented in working tree

## Goal

Open `165.4 / 999.76` by making theory-governance operations observable through
an append-only JSONL ledger and an offline pattern analyzer.

The slice should leave durable evidence for what kinds of theory operations keep
failing, and convert recurring blocker patterns into concrete proposals with
confidence scores instead of leaving them implicit in scattered runtime files.

## What Landed

- new metrics module:
  - `knowledge_hub/theory_metrics.py`
- production theory-operation logging now writes append-only JSONL rows to:
  - global ledger: `runtime/theory_metrics/theory_operations.jsonl`
  - topic-local projection: `runtime/topics/<topic_slug>/theory_operations.jsonl`
- the following production surfaces now log theory-operation metrics:
  - conformance audits
  - theory coverage audits
  - formal-theory audits
  - analytical-review audits
  - topic-completion assessments
  - promotion requests
  - promotion approvals
  - promotion rejections
  - human promotion executions
  - auto-promotion executions
- repeated blocked theorem-facing audits now also emit derived
  `derivation_retry` rows so later loop-detection work has durable evidence
- new offline analyzer surface:
  - `AITPService.analyze_theory_metrics(...)`
- analysis artifacts now materialize automatically to:
  - global:
    - `runtime/theory_metrics/analysis.latest.json`
    - `runtime/theory_metrics/analysis.latest.md`
  - topic-local:
    - `runtime/topics/<topic_slug>/theory_metrics.analysis.json`
    - `runtime/topics/<topic_slug>/theory_metrics.analysis.md`
- analyzer currently turns recurring blocker patterns into proposals with
  confidence scores for:
  - prerequisite-closure debt
  - missing source-anchor debt
  - repeated promotion rejection/blocking
  - repeated derivation retries
  - repeated mechanical-preflight failures

## Verification

- `python -m pytest research/knowledge-hub/tests/test_theory_metrics.py -q`
- `python -m pytest research/knowledge-hub/tests/test_aitp_service.py -q`
- `python -m pytest research/knowledge-hub/tests/test_maintainability_budgets.py -q`
- `python -m pytest research/knowledge-hub/tests -q`

## Outcome

`165.4` now has a durable self-observation slice:

- theory-governance operations no longer disappear into isolated audit files
- recurring blocker patterns are summarized into actionable proposals with
  numeric confidence instead of anecdotal memory
- the data trail for later loop-detection and manifest-governance work now
  exists as a first-class runtime artifact

## Notes

- maintainability budget for `knowledge_hub/aitp_service.py` was adjusted from
  `7400` to `7600` lines after extracting the new theory-metrics analysis
  boundary into its own module; the rationale was updated in
  `research/knowledge-hub/maintainability_budgets.json`

## Remaining Open Work Inside 165.4

- `999.77` derivation loop detection with intervention
- `999.78` manifest-as-truth-source
