evidence-audit
Skill0 starsChanged 2 months agoMITView on GitHub
Audit work before it is shared or called complete, choosing a light decision review, a multi-lens defect search, or a deep high-risk review. Use for “audit”, “red team”, “final review”, “감사”, security and resilience checks, or release readiness. Finding issues is read-only unless fixes are explicitly requested.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- youyu-code/research-pipeline-skillstheir own version, first seen Nov 8, 2026
- luciferprosun/AIOA-spArkHATtheir own version, first seen Oct 25, 2026
- juanitourquiza/shipframetheir own version, first seen Oct 23, 2026
- nino-chavez/agentic-ways-of-workingtheir own version, first seen Oct 13, 2026
- ersinkoc/evidence-audittheir own version, first seen Oct 4, 2026
- crfldrxyz/AuthorOps-Skillstheir own version, first seen Sep 1, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- ersinkoc/evidence-audit★ 4same name
- juanitourquiza/shipframe★ 4same name
- luciferprosun/AIOA-spArkHAT★ 2same name
- nino-chavez/agentic-ways-of-working★ 2same name
- youyu-code/research-pipeline-skills★ 1same name
- crfldrxyz/AuthorOps-Skills★ 0same name
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.
In this repository9 other files it gives its agents.
- absorbSkill
- agent-starterSkill
- aim-before-buildSkill
- converge-planSkill
- decision-sheetSkill
- goal-contractSkill
- harness-auditSkill
- morning-briefSkill
- phase-loopSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
1
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
- Very specific: line 18
Treat delegated findings as CANDIDATE, LIKELY, or REFUTED.
- Distinctive: line 20
If fixes were explicitly requested, assign disjoint paths, keep shared index/manifest files under one owner, then rerun the real project …
“Common” is measured: kept by at least 6 in 10 of the 5 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
Evidence Audit
Choose one depth; do not run all three automatically.
- Light: challenge one design decision with evidence and a counterexample.
- Standard: inspect independent lenses such as security, state consistency, user-journey gaps, and operational recovery.
- Deep: examine an irreversible architecture, security, safety, or migration decision from primary evidence.
Workflow
- Define scope and success criteria. Split lenses or file clusters so they do not overlap unnecessarily.
- Gather candidate findings with exact source locations, a concrete failure scenario, and a proposed decisive check.
- Treat delegated findings as
CANDIDATE,LIKELY, orREFUTED; only the parent can mark a high-severity findingCONFIRMEDafter reopening the source. - Report confirmed findings by severity and include useful refutations so safe paths are visible.
- If fixes were explicitly requested, assign disjoint paths, keep shared index/manifest files under one owner, then rerun the real project checks.
Completion verdict
Return NOT_READY, READY_FOR_REVIEW, or READY_TO_SHARE, followed by executed checks, assumptions, and remaining risk. No evidence means NOT_READY.
Next
Fix confirmed findings and rerun this audit. Capture a reusable lesson only after it has repeated in more than one real task.
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.

