review
Skill1 starsChanged 6 days agoMITView on GitHub
Review an MR/PR or a diff with one system for everyone's code — someone else's MR (comments to post) or the user's own (self-review before asking a reviewer, also run as the pattern gate when the mr skill opens an MR). Applies the repo's review checklist and every known pattern from the review ledgers. Use for "review !N", "review this MR/PR", "self-review", "relire la MR", "check my MR before review".
192 other projects use a version of this.
Adopted by
Most recently taken up by
- microsoft/veronatheir own version, first seen Nov 8, 2026
- microsoft/FluidFrameworktheir own version, first seen Nov 8, 2026
- charliee1w/consolidation-memorytheir own version, first seen Nov 8, 2026
- jisuzzzz/haechitheir own version, first seen Nov 8, 2026
- apokamo/kajitheir own version, first seen Nov 8, 2026
- dirien/yet-another-agent-harnesstheir own version, first seen Nov 8, 2026
Used with
Projects with it also use close-spec, design-with-images and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
dzhngSkillclose-spec
When a spec under specs/<feature>/ is done shipping (a write-spec build, or any planned task that produced a spec), archive it to specs/done/ and rewrite it from a build-plan into a durable rationale — the why, the principles, the invariants — pointing back to the real code for the how. Use when implementation has landed and the plan no longer matches what shipped, or the user says a feature/spec is finished. Pairs with [write-spec](../write-spec/SKILL.md) (the plan this closes) and [review](../review/SKILL.md) (run before closing).
Also in 5 projects that use this
dzhngSkilldesign-with-images
Explore UI and visual design with image generation, then iterate the real implementation against the user-selected concept. Use when brainstorming visual directions, offering mockup variants, or implementing an approved generated design; do not stop at the mockups or the first code approximation.
Also in 5 projects that use this
LambenthanSkilldaily-arxiv
每日从 arXiv 拉取新论文,过滤相关性,auto-ingest 高优先级论文,检测 SOTA 更新
Also in 5 projects that use this
LambenthanSkillexp-eval
实验判决门:Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges
Also in 5 projects that use this
- +3 more from dzhng
- +5 more from Lambenthan
Other versions192 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 192
- garrytan/gstack★ 141ksame name
- ansible/ansible★ 71ksame name
- docling-project/docling★ 70ksame name
- traefik/traefik★ 65ksame name
- ClickHouse/ClickHouse★ 51ksame name
- temporalio/temporal★ 24ksame name
- tisfeng/Easydict★ 15ksame name
- juicedata/juicefs★ 15ksame name
- elie222/inbox-zero★ 13ksame name
- gitkraken/vscode-gitlens★ 10ksame name
182 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 157 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 21
Read around each change, not only the hunks: sibling functions, the same pattern elsewhere, callers.
- Very specific: line 22
If the repo has a code graph (codebase-memory MCP server: detect_changes against the target branch, trace_path inbound for callers of …
- Distinctive: line 28
Every other ledger row: run its "how to catch it" on the diff for real (the greps, the sibling comparison, docstring vs code).
- Distinctive: line 40
Never add AI attribution anywhere.
“Common” is measured: kept by at least 6 in 10 of the 157 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Review
Same checks whoever wrote the code. The ledgers hold what reviewers keep flagging on the user's MRs, so applying them to every diff is how known patterns stop reaching reviewers.
0. Locate
- Hub =
readlink -fof this skill's base directory, two levels up. Registry:<hub>/local/projects.yaml. - Project from the MR's forge path or the current repo; work in its
path(read branch files withgit show origin/<branch>:<path>, no checkout needed). - Mode: self when the MR author is the user (
forge_userin the registry) or there is no MR yet; otherwise peer. - Load and follow:
- the repo's review skill if any (
<repo>/.claude/skills/*review*/SKILL.mdor<hub>/local/skills/<project>/*review*/): checklist and writing style win; - the ledgers
<hub>/local/reviews/common.mdand<hub>/local/reviews/<project>.md.
- the repo's review skill if any (
1. Gather
- MR: description, pipeline status (and failed job logs), target branch. Diff:
git fetch origin && git diff origin/<target>...origin/<branch>. - Read around each change, not only the hunks: sibling functions, the same pattern elsewhere, callers.
- If the repo has a code graph (
codebase-memoryMCP server:detect_changesagainst the target branch,trace_pathinbound for callers of changed functions,search_graphacross Python/Terraform/shell), use it for impact and callers instead of grepping blind. - Other open MRs touching the same files or keys: note clashes.
2. Check
- The repo checklist, item by item.
- Mechanical rules first:
<hub>/bin/crow-review-check <repo checkout> --base origin/<target>runs the regex rows of the ledgers (local/reviews/rules/) on the diff and exits 1 on a hit. Report its hits as findings and mark those rows hit/clean from its output. The rows without a rule stay in the next step. - Every other ledger row: run its "how to catch it" on the diff for real (the greps, the sibling comparison, docstring vs code). Mark each row hit / clean / n/a with one line of evidence. No row skipped silently.
- Then the usual: correctness, failure paths, tests that prove the change, infra impact per environment (destroy/replace, data outside Terraform, permissions).
3. Output
First line: Patterns: N checked, K hits, M n/a.
Peer mode: comments ready to post, grouped blocking / suggestions / nits / questions, each file:line + text in the repo skill's style and the reviewer's usual language. One-line verdict. Post only when asked, as diff-line discussions, not one big note.
A hit that matches a ledger row on someone else's MR is not added to the user's ledger (it tracks the user's own MRs).
Self mode: a fix list, most important first: file:line, problem, pattern (ledger row or new), proposed fix. Questions that need the user's decision last.
On approval: fix on the branch, commit in the repo's convention, push. A new pattern found here (not in any ledger) gets a row in the project ledger with self-review as source, so it's checked next time too.
Never add AI attribution anywhere.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

