ABOUTME: Cross-repo learning loop, turns scattered LEARNING.md into ranked harness changes

ABOUTME: Deterministic ingest (script) + recurrence detection (agent) + change-contract output

Learning Loop

Turns the LEARNING.md retrospectives scattered across every repo into process improvements. The value is not the count of lessons, it is the recurrence: a failure shape that appears in one repo is an anecdote, the same shape across repos is a signal worth a mechanical fix.

Two halves, by design:

1. Ingest (deterministic, no LLM). scripts/learning_corpus.py discovers all LEARNING.md, dedupes working copies, splits each into atomic learnings, emits a JSONL corpus. Reproducible and free.
2. Recurrence (agent pass). An agent clusters the corpus by failure shape, keeps only patterns spanning two or more distinct repos, ranks them, and proposes one harness action per pattern with a six-field change-contract.

This pairs with, but is distinct from, related tools. learning-docs writes a single project's LEARNING.md (the input to this loop). harness-mechanic reads execution traces and token baselines (mechanical signals: cost, tool-call shape); this loop reads the human retrospectives (what actually went wrong and why). knowledge-sync promotes vault patterns to skills; this loop promotes cross-repo failure-modes to harness changes.

When to run

On a human schedule (monthly, or after a milestone closes across several repos), never autonomously. The corpus is cheap to rebuild; the agent pass costs tokens, so do not loop it.

Cadence guard