this-is-fine
Skill1 starsChanged 4 months agoMITView on GitHub
Run a small pinned regression suite to detect whether an existing Agent Skill's triggers, tool routing, permissions, output contract, or versioned guidance drifted after a model, harness, tool, or API update. Use when upgrading a model or agent, checking a release, scheduling compatibility checks, or investigating why a formerly working skill changed behavior. Do not use for general skill slimming or to prove broad efficacy; those need a structural audit or paired trial.
In this repository2 other files it gives its agents.
- skill-issueSkill
- trust-me-broSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
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 14
Read the skill and its last known-good canary record.
- Distinctive: line 20
Use fixtures or sandboxes.
- Distinctive: line 25
Keep task inputs, permissions, tools, reasoning, and timeout stable.
- Distinctive: line 35
Run the last known-good environment first when practical, then the candidate environment.
- Distinctive: line 54
Do not rewrite unrelated guidance or silently update expected output to make the run green.
- Very specific: line 56
Use $trust-me-bro when available before claiming the new version improves efficacy.
≈ “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
This Is Fine
Detect compatibility drift cheaply. Do not turn a canary into a full benchmark.
The coffee is optional. The pinned regression cases are not.
Prepare
- Read the skill and its last known-good canary record.
- Pin the old and new model or harness versions when both remain available. Otherwise pin the new environment and compare against stored expected observations.
- Select five to ten cheap cases from references/canary-contract.md.
- Use fixtures or sandboxes. Do not let a canary mutate production or publish externally.
Run
Keep task inputs, permissions, tools, reasoning, and timeout stable. Use fresh contexts and capture raw evidence for:
- trigger activation and near-miss non-activation;
- preferred tool route and fallback;
- approval or destructive-action boundary;
- output schema or completion contract;
- one version-sensitive fact or command;
- one known historical failure.
Run the last known-good environment first when practical, then the candidate environment. A missing old runtime is a limitation, not permission to invent a comparison.
Classify
- Trigger drift: activation becomes too broad, too narrow, or ambiguous.
- Capability drift: the same route no longer achieves the outcome.
- Tool drift: a tool, argument, path, or fallback changed.
- Safety drift: approval, validation, or write boundaries weaken.
- Knowledge drift: pinned instructions conflict with current primary sources.
- Harness drift: discovery, progressive disclosure, or resource loading changes.
Act
- Fail the canary on any safety regression or broken core outcome.
- Propose the smallest correction at the source of drift.
- Do not rewrite unrelated guidance or silently update expected output to make the run green.
- Use
$trust-me-browhen available before claiming the new version improves efficacy. Otherwise compare the same pinned task in isolated baseline and candidate contexts with one declared verifier. A canary alone never proves uplift.
Return
Report the environment delta, case-by-case observation, raw evidence, drift class, severity, smallest proposed fix, and whether release should pass, warn, or block.
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.

