ai-learn

name	ai-learn
description	Extracts lessons from merged PR review feedback by analyzing what reviewers caught, identifying missed checks, and writing entries directly to LESSONS.md. Trigger for 'the AI keeps doing X wrong', 'learn from this PR', 'what patterns did reviewers catch', 'update our standards from feedback'. Not for in-session observation; use /ai-session-watch instead. Not for skill-level rewrites; use /ai-skill-improve instead.
effort	mid
argument-hint	single [pr]|batch
tags	["meta","learning","continuous-improvement"]
model_tier	sonnet
mirror_family	antigravity-skills
generated_by	ai-eng sync
canonical_source	.claude/skills/ai-learn/SKILL.md
edit_policy	generated-do-not-edit

Learn

Continuous improvement from delivery outcomes: analyzes merged PRs to find where AI missed what human reviewers caught, identifies false positives, and writes lessons directly to .ai-engineering/LESSONS.md. Command: /ai-learn single <pr>|batch (single = after a PR merge; batch = periodic).

Workflow

Step 0: read .ai-engineering/LESSONS.md for pre-existing patterns; load stack contexts: read .ai-engineering/manifest.yml providers.stacks and apply .ai-engineering/overrides/<stack>/conventions.md for each stack.

Two modes — single <pr> (one PR) and batch (all merged PRs since last lesson update) — share one loop: read PR comments + diff → classify each into a Pattern Category → dedupe against existing LESSONS.md entries → append new lessons with category + evidence link → sweep for AGENTS.md proposals. Authoritative per-mode steps below.

Modes

single -- Analyze one merged PR

1. Fetch PR data -- gh pr view <pr> --json body,reviews,comments,files,additions,deletions.

2. Collect AI findings -- read the AI-generated PR description, guard advisories, and verify results from the PR.

3. Collect human feedback -- extract all review comments, requested changes, and approval notes.

4. 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

5. Write lesson -- for each actionable pattern found (AI miss, false positive, or novel insight), append a lesson entry to .ai-engineering/LESSONS.md:

   ### [Pattern name derived from PR analysis]
   
   **Context**: [What happened in PR #NNN — the specific review feedback]
   **Learning**: [The pattern or rule extracted from the feedback]
   **Rule**: [Actionable instruction for future sessions]
   

   Only write lessons for patterns that are repeatable and actionable. Skip one-off issues specific to a single PR.

batch -- Process unanalyzed merged PRs

1. Read tracking marker -- check .ai-engineering/LESSONS.md YAML frontmatter for lastAnalyzedAt field. If absent, this is the first batch run.
2. Find unanalyzed PRs -- git log --merges --since=<lastAnalyzedAt> --format="%H %s". Extract PR numbers from merge commit messages. If git log --merges yields no results (e.g., squash-merge workflow), fall back to gh pr list --state merged --json number,mergedAt filtered by lastAnalyzedAt.
3. Process each -- run single-mode analysis for each unanalyzed PR.
4. Update marker -- set lastAnalyzedAt: <current ISO date> in LESSONS.md frontmatter (add frontmatter if absent).
5. Summary -- report total PRs analyzed, lessons written, and emerging patterns.

Pattern Categories

Pattern	Example	Action
Missed check	AI never flags missing error handling in async code	Write lesson with Rule for future sessions
Over-flagging	AI flags every single-letter variable in list comprehensions	Write lesson noting the exception
Missing context	Reviewers always explain why a specific pattern is used in this codebase	Write lesson adding the context
Style drift	Reviewers consistently request a style AI does not enforce	Write lesson with the style rule

AGENTS.md proposal mode (spec-121)

Single-PR analysis writes to LESSONS.md. Procedural memory (AGENTS.md, CONSTITUTION.md) is the durable layer agents read on every session — when a category of lessons crosses threshold, it should be reinforced there, not buried in LESSONS.md.

After every batch run (or at the end of a single run), perform a category sweep:

1. Group all lessons in .ai-engineering/LESSONS.md by Pattern Category (Missed check / Over-flagging / Missing context / Style drift / custom).
2. For any category whose count is ≥ 5 AND that has not already been reflected in AGENTS.md (grep AGENTS.md for the category name or a representative phrase), draft a proposal block.
3. Append the proposal to .ai-engineering/state/agents-proposals.md (create if absent). Never edit AGENTS.md directly — same constraint as /ai-dream (D-118-04). Humans review and merge proposals manually via PR.

Proposal block format:

## Proposal — <ISO date> — <Category name>

**Trigger**: <N> lessons in category "<Category>" since <oldest>; AGENTS.md does not yet codify this rule.

**Suggested AGENTS.md addition** (under section `## Hard rules` or appropriate):

> <single-sentence imperative rule derived from the lessons>

**Evidence** (lesson titles, PR refs):
- <lesson 1>
- <lesson 2>
- ...

**Action**: open a PR adding the rule above to AGENTS.md if accepted.

Emit a framework_operation event with operation=agents_proposal_drafted, category=<name>, lesson_count=<N> so the audit chain records each proposal cycle.

Examples

User: "synthesize lessons from everything merged this sprint" → /ai-learn batch walks merged PRs since last lesson update, runs single-mode analysis per PR (classify into pattern categories, dedupe, append to LESSONS.md), and drafts AGENTS.md proposals when categories accumulate enough evidence.

Integration

Called by: user directly, post-merge automation. Reads: gh pr view, LESSONS.md. Writes: LESSONS.md (append-only). See also: /ai-note (individual findings), /ai-session-watch (in-session corrections), /ai-skill-improve (acts on accumulated lessons).

$ARGUMENTS