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Use when analyzing merged PRs for AI blind spots, extracting patterns from human reviewer feedback, or applying accumulated learnings to context files.
Passed 18 of 21 test cases with the skill, and 9 of 21 without it.
and 10 more in the full report
10 fixed9 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
The automated reviewer flagged an missing authorization check on an API endpoint in PR #301, and the human reviewer commented 'Good catch, fixing now'. Which category does this belong to?
| Checked | Without skill | With skill |
|---|---|---|
| Labels the finding as AI hit | Missed | Passed |
The instructions your model reads when the skill loads.
Continuous improvement from delivery outcomes. Analyzes merged PRs to find where AI missed what human reviewers caught, identifies false positives, and synthesizes recurring patterns into context updates. The feedback loop that makes the framework smarter over time.
/ai-learn single <pr>|batch|applyFetch PR data -- gh pr view <pr> --json body,reviews,comments,files,additions,deletions.
Collect AI findings -- read the AI-generated PR description, guard advisories, and verify results from the PR.
Collect human feedback -- extract all review comments, requested changes, and approval notes.
Cross-reference -- compare AI findings with human feedback:
| Category | Description |
|---|---|
| AI miss | Human reviewer found an issue AI did not flag |
| False positive | AI flagged something human reviewer dismissed or overrode |
| AI hit | AI flagged an issue human reviewer agreed with |
| Novel insight | Human added context AI could not have known |
Record -- append learning entry to .ai-engineering/learnings/index.jsonl:
{"pr": 123, "date": "2026-03-19", "misses": 2, "false_positives": 1, "hits": 5, "patterns": ["missed-null-check", "over-flagged-naming"]}
index.jsonl.test cases passed, vs 9 / 21 without
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