paulund/learn-pr-learnt

PR Learnt — Review Comment Teaching Loop

Scan PR review comments from the last 7 days across all your repositories and extract non-obvious lessons worth keeping. The goal is to make future sessions smarter, not to record every comment.

What's worth capturing

Look for:

• Corrections — reviewer requested changes: a pattern flagged, a wrong approach called out
• Validated approaches — reviewer approved with a substantive comment affirming the approach
• Decisions — a discussion in a PR that reached a conclusion about how to do something
• Your own review feedback — things you called out on others' PRs (they reveal your standards)

Ignore:

• Formatting/whitespace nitpicks
• Bot comments (Dependabot, CI bots, auto-reviewers)
• Trivial "LGTM" or "Looks good" with no body
• Things already in AGENTS.md, MEMORY.md, or global skill files

How to run

1. Find recent PRs

gh search prs --involves @me --updated ">$(date -v-7d +%Y-%m-%d 2>/dev/null || date -d '7 days ago' +%Y-%m-%d)" --json number,repository,title,url --limit 50

2. For each PR, fetch review comments

# Inline code review comments
gh api repos/{owner}/{repo}/pulls/{number}/comments --jq '[.[] | {body, path, user: .user.login}]'

# Review-level summaries (state: APPROVED, CHANGES_REQUESTED, COMMENTED)
gh api repos/{owner}/{repo}/pulls/{number}/reviews --jq '[.[] | {state, body, user: .user.login}]'

# General PR discussion
gh api repos/{owner}/{repo}/issues/{number}/comments --jq '[.[] | {body, user: .user.login}]'

Focus first on any review with state: "CHANGES_REQUESTED" — those are corrections.

3. Deduplicate

Read the project MEMORY.md before saving anything. Skip lessons already captured.

4. Apply the decision tree

Is this a universal rule for a language, framework, or tool?
├── Yes → the relevant global skill file in ~/.agents/skills/
└── No  → Is it specific to this codebase's architecture or conventions?
           ├── Yes → {repo}/AGENTS.md
           └── No  → Is it about the user, or project context?
                      ├── Working preference / correction → auto-memory (type: feedback)
                      ├── Project fact / decision → auto-memory (type: project)
                      └── Personal context about the user → auto-memory (type: user)

How to write each type

Auto-memory (feedback / user / project)

The project memory directory is shown in your system prompt context — look for the path ending in /memory/. This is project-specific and changes per repo.

File naming: feedback_<slug>.md, project_<slug>.md, user_<slug>.md

Frontmatter:

---
name: learn-pr-learnt
description: One-line hook used to decide relevance in future sessions — be specific
type: feedback | project | user
---

Body for feedback: Lead with the rule. Then Why: (the reason given). Then How to apply: (when this kicks in).

Body for project: Lead with the fact or decision. Then Why: (the motivation). Then How to apply: (how this should shape suggestions).

After writing each memory file, add a one-line pointer to MEMORY.md under the right section heading. Check MEMORY.md first — update an existing entry rather than duplicating.

Global skill files

Append to the relevant file at ~/.agents/skills/. Keep additions concise — one rule, one short explanation.

AGENTS.md

Add to the most relevant section, or create a new one.

Output to user

After saving:

• What you found (if anything)
• Where each lesson was saved
• If nothing was worth saving, say so clearly

Keep it to a few bullet points. No waffle.