factory-learn

factory-learn

What this skill does

factory-learn orchestrates the continuous improvement learning loop of the software factory. It coordinates individual learning-related skills (team-init, team-levelup, change-init, change-clarify, change-publish, team-repair, evals-analyze) to transition draft directives into verified, published, and minimal team context assets.

It operates as a Kind-A DAG orchestrator in alignment with the shared executor engine contract in factory-mission/references/executor.md.

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When to use

• You want to extract and compile hard-won session learnings into your team's centralized team-ai-directives repository.
• You want to mine git commit history to capture the rationale (ChDRs) behind past reverts and hotfixes.
• You want to run "Build to Delete" (Harness Decay checks) to prune redundant rules.

When NOT to use:

• For product-level specification or development (use factory-product or factory-mission instead).
• If the team directives repository is completely unconfigured (run /team-setup first).

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Lifecycle DAG & Step Resolution

factory-learn implements a fixed named-skill DAG (fixed step resolution):

Session Learnings Route (default on session-end)

1. specify (generate phase) -> Invoke team-levelup to extract candidate Context Directive Records (CDRs) and compliances from the active session.
2. clarify⭐ (clarify phase) -> Invoke team-levelup to review pending CDRs. Enforces the evals-regression gate (running the compliance goldset as the verify sub-phase to ensure no quality degradation).
3. publish (build phase) -> Invoke team-levelup to package accepted CDRs, index them, and compile a draft PR targeting the team-ai-directives repository.
4. prune (analyze phase) -> Runs the cleanup bot over the directive store to detect and propose deprecations of superseded, contradictory, or stale rules. Deprecations feed back to team-levelup.