review
Skill9 starsChanged 7 months agoMITView on GitHub
Deep review of a knowledge area — find gaps, contradictions, staleness, and suggest improvements
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 159 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
If no area is specified, ask which area to review.
- Very specific: line 20
Read ALL files in the specified knowledge/ subdirectory
- Very specific: line 21
Read ion/state.md for current context
- Very specific: line 60
Add deferred fixes to ops/action-items.md
“Common” is measured: kept by at least 6 in 10 of the 159 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 — Deep Knowledge Review
Conduct a thorough review of a specific knowledge area.
Usage
The partner specifies an area: /review strategy, /review goals, /review characters, etc. The valid areas depend on the chosen mode (see ion/state.md).
If no area is specified, ask which area to review.
Process
- Dispatch the Librarian sub-agent to scan the area for structural issues, contradictions, and staleness
- Dispatch the Strategist sub-agent if the area involves planning, positioning, or external context (e.g., strategy, intelligence, goals, plot) — for deeper analytical review
- Read ALL files in the specified
knowledge/subdirectory - Read
ion/state.mdfor current context - Cross-reference with adjacent knowledge areas that might reveal inconsistencies
Analysis
For each file in the area, evaluate:
- Currency: Is the information still accurate? Are dates outdated?
- Completeness: Are there obvious gaps or unanswered questions?
- Consistency: Does anything contradict other files in the knowledge base?
- Actionability: Is the knowledge driving decisions, or just sitting there?
- Depth: Is the level of detail appropriate for the partner's current situation?
Output
# Review: [Area] — [Date]
## Overall Health: [Strong / Adequate / Needs Attention / Critical Gaps]
## What's Solid
[Well-documented, current, useful knowledge]
## Gaps
[What's missing or too thin]
## Stale
[What needs updating — with specific suggestions]
## Contradictions
[What conflicts with other knowledge — cite both sources]
## Recommendations
[Prioritized actions, from most to least impactful]After Review
- Save the review to
ops/reviews/YYYY-MM-DD-review-[area].md - Offer to make recommended updates immediately (following the three-tier model)
- Add deferred fixes to
ops/action-items.md
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★ 139ksame 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★ 9.9ksame 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.
Used alongsideProjects with it also use daily-arxiv, exp-eval and 9 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.
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
SethGammonSkillascii-diagram
Generate perfectly aligned ASCII diagrams — architecture, flow, sequence, box-and-arrow. Uses a programmatic character-grid approach so alignment is guaranteed by math, not token prediction. Includes post-render verification.
Also in 4 projects that use this
DAIR-AutoResearchSkillexp-pilot-eval
Pilot result evaluation — read pilot results, apply success criteria, update idea page (pilot_result, failure_reason if failed), generate PILOT_VERDICT_REPORT. Called by /ideate Phase 5 after /exp-pilot-run.
Also in 4 projects that use this
DAIR-AutoResearchSkillexp-pilot-run
Pilot experiment execution — read Pilot Spec YAML, write pilot code, run experiment(Confirm with the user before operation and require the applicant to conduct manual inspection), return results. Called by /ideate Phase 5. Does NOT modify wiki pages or judge pass/fail.
Also in 4 projects that use this
- +5 more from Lambenthan
- +1 more from DAIR-AutoResearch
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
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.

