debugging-with-evidence
Skill33 starsChanged 34 days agoApache-2.0View on GitHub
Diagnose unexpected behavior with a reproduction, competing explanations and experiments that separate them before fixing. Use for bugs, failed checks or repeated unsuccessful fixes; attribute branch failures only with suitable comparison evidence.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- okgrit/done-is-a-claimword for word, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 1
- okgrit/done-is-a-claim★ 33100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
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 repository16 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- acceptance-designSkill
- checking-deliverySkill
- collecting-worker-resultsSkill
- evidence-freshnessSkill
- executing-plansSkill
- implementing-with-testsSkill
- planning-changesSkill
- public-claimsSkill
- reading-measurementsSkill
- responding-to-reviewSkill
- resuming-workSkill
- reviewing-changesSkill
- using-done-is-a-claimSkill
- whose-redSkill
The file
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 10 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈10
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
Keep the investigation tied to the user's outcome and authorized file scope.
- Distinctive: line 16
Read the complete error and inspect the affected artifact and relevant diff.
- Distinctive: line 22
Use read-only inspection to answer state questions.
- Distinctive: line 27
Write a small set of plausible hypotheses based on the observation.
- Distinctive: line 37
When evaluating a detector or classifier, establish that a known-good case can produce a negative result.
- Very specific: line 42
If branch versus baseline attribution matters, use whose-red at the location the host actually exposes.
- Distinctive: line 50
When evidence supports a cause, make the smallest change that addresses its class within the authorized scope.
- Distinctive: line 54
Avoid tests that only repeat implementation values without an independent basis.
- Distinctive: line 56
If a planned fix fails, compare its prediction with the new observation and revise the hypothesis.
- Very specific: line 66
Use discovered reviewing-changes when useful.
- Distinctive: line 67
After review fixes, run affected checks and review changed or unresolved findings.
- Distinctive: line 69
Before reporting success, confirm evidence describes the final inputs and rerun final acceptance yourself.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 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
Debugging with evidence
An observed failure establishes that something happened under particular conditions. Find the mechanism that explains it before changing another thing. Keep the investigation tied to the user's outcome and authorized file scope.
Capture the failure
- Record expected and observed behavior, exact command or user action, revision, dirty inputs, environment, relevant order or seed and evidence path.
- Read the complete error and inspect the affected artifact and relevant diff. Missing paths, absent tools, empty test collection and permission errors may explain an unrunnable check; they do not establish successful behavior.
- Reproduce with the smallest scope that still preserves the failure. Retain the original run: narrowing can remove order, shared state or boundary effects.
- Register cleanup before an experiment that creates temporary resources. Use read-only inspection to answer state questions; do not reset or destroy the working tree to learn what was changed.
Test explanations
Write a small set of plausible hypotheses based on the observation. For each, name the predicted result and the observation that would contradict it. Choose an experiment whose outcomes separate at least two explanations. Repeating the same failed run without changing a discriminating condition adds little evidence.
Change one relevant condition where feasible and capture the actual outcome. For an intermittent failure, retain repetitions and their conditions; one passing retry is not proof of a fix. Instrument only what is needed and remove temporary instrumentation when it no longer contributes to the delivered behavior.
When evaluating a detector or classifier, establish that a known-good case can produce a negative result. A check that flags everything may be measuring itself. Inspect differences in inputs, configuration and failure signatures before assigning cause from test names, counts, labels or timing alone.
If branch versus baseline attribution matters, use whose-red at the location
the host actually exposes. If unavailable, compare identified trees with matched
commands, environment and failure signatures, using a clean control when feasible.
Report extra branch failures and unmatched conditions separately. A shared failure
on one baseline supports a limited historical finding, not innocence of the change.
Fix the supported mechanism
When evidence supports a cause, make the smallest change that addresses its class within the authorized scope. Inspect related call sites and affected inputs; one repaired example may leave the same defect elsewhere. Where warranted, add a regression check that would reject the old behavior for the observed reason. Avoid tests that only repeat implementation values without an independent basis.
If a planned fix fails, compare its prediction with the new observation and revise the hypothesis. Do not stack unrelated guesses. If experiments stop distinguishing explanations, summarize what is known, seek a better observation or name the concrete unavailable prerequisite. Continue independent work if useful.
Verify the explanation and result
Rerun the reproduction and relevant regression scope, then the agreed acceptance
and broader checks on the candidate. Inspect the actual output and capture each
command's own exit code. Review the diff for scope and temporary artifacts.
Use discovered reviewing-changes when useful; self-review remains explicit.
After review fixes, run affected checks and review changed or unresolved findings.
Before reporting success, confirm evidence describes the final inputs and rerun final acceptance yourself. State the supported cause, rejected alternatives, changed files, observed checks and evidence paths. Keep unavailable comparisons, remaining intermittent behavior and untested boundaries explicit.
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.

