review-output
Skill49 starsChanged 3 months agoMITView on GitHub
Default reviewer criteria for the with_review tool. Generic quality bar — accuracy, intent-match, no hallucinations, clarity. Use as the criteria.skill argument when calling with_review without a domain-specific reviewer skill.
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use these criteria when reviewing the worker's draft.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Review criteria — generic quality bar
Use these criteria when reviewing the worker's draft. Issue exactly one verdict per iteration:
- APPROVE — passes all checks cleanly. Ship it.
- REVISE — 1-2 specific fixable issues. Be concrete: "the second paragraph claims X but the source says Y" not "make it better".
- REJECT — fundamentally wrong shape. Examples: answered the wrong question, wrong format, contradicts the task entirely. Use sparingly; most issues are revisable.
Checks
- Accuracy — every factual claim is grounded in the provided context or independently verifiable. No invented stats, dates, names, URLs, or quotes. If the worker hedged ("approximately", "in some cases") that's fine; if they stated as fact something not in the source, that's REVISE.
- Matches intent — does this actually answer what the user asked for, not the adjacent question? An email asking for a refund should request a refund, not apologise. A code review should flag bugs, not document the code.
- Tone matches situation — formal email needs formal language; internal Slack message can be casual. Look for tone mismatches that would jar the recipient.
- No hallucinations — invented references, made-up function names, fake API endpoints, fictional people. These are always REVISE — the worker can usually fix by removing the offending claim.
- Clarity — could a reasonable reader act on this without asking questions? Vague references ("the thing we discussed"), undefined terms, missing context — REVISE.
- Length appropriate to task — a one-line summary task should produce a one-liner, not a paragraph. A detailed report should not be three sentences. Both directions are REVISE.
Verdict format
Respond with ONE LINE in this exact shape:
VERDICT: APPROVE — passes all checks
VERDICT: REVISE — second paragraph claims 23% growth but source says 18%
VERDICT: REJECT — answered "what's our pricing" instead of "draft a thank-you email"The em-dash (—) or hyphen (-) is the parser delimiter — use either.
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-asks-tasksSkill
- caretakerSkill
- code-reviewSkill
- compare-optionsSkill
- create-reportSkill
- csv-analyseSkill
- document-qaSkill
- draft-emailSkill
- enrich-errorSkill
- extract-structured-dataSkill
- fact-checkSkill
- git-diff-summariserSkill
- librarian-curateSkill
- meeting-notesSkill
- morning-briefSkill
- plan-taskSkill
- reflectSkill
- remember-conversationSkill
- reverieSkill
- review-code-securitySkill
- review-email-toneSkill
- review-summary-faithfulnessSkill
- rewrite-for-audienceSkill
- route-findingSkill
- routine-health-checkSkill
- save-research-docSkill
- score-importanceSkill
- summarise-urlSkill
- web-researchSkill
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.

