Stuff: Skill for Claude Code

.agents/skills/review-lessons/SKILL.md

review-lessons is a skill for Claude Code, Codex from kyleve/Stuff. It costs 35 tokens per session (1,731 once invoked), scanned A, original, Apache-2.0.

A procedure for reviewing pull-request comments and later fixes to find recurring mistakes and improve repository guidance. A pull request is a proposed code change that others review before it is merged.

In plain words
What is it for?
Use it for a monthly review retrospective or when repeated review failures need investigation across recent pull requests.
Why use it?
It turns repeated review problems into documented improvements instead of treating each comment as an isolated incident. It also compares earlier audit changes with what actually reached the codebase.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md; $skill-name invocation.

This is kyleve/Stuff's own configuration. It tells Claude Code and Codex how to work on Stuff itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Stuff configures →

View source ↗ kyleve/Stuff
Reuse

Borrowing it

Nothing to install: this file belongs to kyleve/Stuff. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/kyleve/Stuff/main/.agents/skills/review-lessons/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/kyleve/Stuff

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for review-lessons

README.md
[![agentmods](https://agentmods.dev/badge/skills/kyleve/stuff/review-lessons/github.svg)](https://agentmods.dev/skills/kyleve/stuff/review-lessons)
Your own site
<a href="https://agentmods.dev/skills/kyleve/stuff/review-lessons"><img src="https://agentmods.dev/badge/skills/kyleve/stuff/review-lessons/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for review-lessons

Your own site · 80×15
<a href="https://agentmods.dev/skills/kyleve/stuff/review-lessons"><img src="https://agentmods.dev/badge/skills/kyleve/stuff/review-lessons.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,731 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.01731
Opus 5.5 $0.00014 $0.00692
Sonnet 5.5 $0.00007 $0.00346
Haiku 4.5 $0.00003 $0.00173

Measured 2d ago against content hash 60389ae29733, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

review-lessons scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/review-lessons/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Read the root AGENTS.md and github-workflow before changes. Use this procedure monthly, or after a recurring failure needs investigation. The weekly todo-triage pass owns backlog maintenance and module audits. code-review owns review of a current PR or completed branch.

Establish the review window

  1. Inspect the checkout and fetch origin/main. Unless the user specifies a base or stack, use a clean branch from current main. Search open PRs for an unfinished review-lessons audit before creating another one. If one exists, continue it through the GitHub workflow.
  2. Use the requested period, or start from the last complete audit's recorded end time. Find that time in its PR body or accessible run summary. If no complete record is available, review the previous three months. Fix an end time in UTC before collection. Record both boundaries and the source revision.
  3. Use three months of context to identify recurring patterns, including lessons from the previous audit. Treat that context as an overlap, not as new evidence on every run. Compare prior audit changes with current source to identify which changes actually landed.

Collect and verify evidence

  • Find PRs updated during the review window, including older, closed, and still-open PRs. Read inline threads, submitted reviews, conversation comments, and follow-up fixes. Include resolved threads and later corrections to the original feedback.
  • Include completed agent-review reports available in the current task or linked to the reviewed PRs. Preserve findings fixed before publication when the review report or fix history records them. Record inaccessible or unrecorded reviews as a coverage limit. A PR-only collection cannot establish complete agent-review coverage.
  • Use scoped TODOs.md files, including completed items, and dated MODULE_AUDIT.md reports to locate agent-found issues and fixes. Verify them against source and history. A backlog entry or stale audit is not proof of a current defect.
  • Collect clarification questions about scope, affected callers, validation, and design rationale. Compare them with the description and context available at the time of review. If the earlier description is unavailable, record that limit. Do not infer an earlier omission from the current body.
  • Paginate the PR list and every comment, review, and thread collection. Split searches that reach an API result cap. Filter individual events by the recorded window, allowing overlap at the start boundary. Include fixes without review comments from the period's merged PRs and commit history.
  • Trace each candidate lesson to the final change and current source. Record the comment, fix, affected behavior, existing guidance, and any executable guard. Treat unmerged proposals and declined feedback as context, not established repository policy.
  • Check recovered opening or pre-review revisions when commits were rewritten. A correction may be folded into the final PR's first commit without a separate review comment. Record unavailable earlier revisions as a coverage limit.
  • Classify by content and outcome, not by author. Apply the same evidence standard to human and agent findings. Separate substantive findings and clarification questions from status updates, screenshots, suggested review focus, and replies without a new finding. Raw comment volume is not a defect count. Inspect later corrections before counting a finding as confirmed. Deduplicate reports of the same defect across review sources. Repeated mentions are not independent recurrences.
  • Before classifying a missed rule, verify that the rule existed when the offending change was made. Use Git history for the relevant instruction and code. Distinguish a missing rule from a rule added after the defect.
  • Mark missing pages, unavailable sources, and unverified outcomes explicitly. An incomplete collection cannot establish that no changes are needed. Do not advance the last complete audit's end time from a partial run.

Read the full file on GitHub · 131 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago Changed · +25 lines · +3 tokens per session 60389ae29733
  2. 11d ago Changed · +5 lines 83d4ba3b990e
  3. 22d ago First seen · 101 lines · 32 tokens per session scan A 4b3c0c222610

Subscribe to this mod's changes

review-lessons is a skill published in the GitHub repository kyleve/Stuff (4 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,731 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-08.

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