corpus-test
Skill7 starsChanged 9 months agoMITView on GitHub
Run corpus regression tests against real-world OpenAPI specs. Usage: /corpus-test [short]
In this repository9 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- plugin/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- gen-testSkill
- prepare-releaseSkill
- prereviewSkill
- publish-releaseSkill
- quality-gateSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
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
Run integration tests against the corpus of real-world OpenAPI specifications.
- Distinctive: line 17
Use the Glob tool to check for corpus files:
- Distinctive: line 23
If no corpus files found, download them:
- Distinctive: line 76
If failures are found:
≈ “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
corpus-test
Run integration tests against the corpus of real-world OpenAPI specifications.
Usage:
/corpus-test— run all corpus tests/corpus-test short— exclude large specs (faster)
Step 1: Check Corpus Availability
Use the Glob tool to check for corpus files:
pattern: testdata/corpus/*.{json,yaml,yml}If no corpus files found, download them:
make corpus-downloadStep 2: Run Corpus Tests
Based on the mode argument:
Full mode (default):
make test-corpusShort mode (argument = "short"):
make test-corpus-shortCapture and analyze the output.
Step 3: Analyze Results
Note: Corpus specs are real-world documents (Plaid, DigitalOcean, GitHub, etc.) that often contain validation errors intentionally. Validation failures are expected — the key metric is whether the fixer resolves them.
For each corpus spec, report:
- ✅ Parse/validate/fix succeeded
- ❌ Failures with error details (fixer failed to resolve validation errors)
- ⚠️ New warnings compared to expected behavior
Step 4: Report
## Corpus Test Results
| Spec | Parse | Validate | Fix | Notes |
|------|-------|----------|-----|-------|
| petstore.yaml | ✅ | ✅ | ✅ | |
| github-api.json | ✅ | ❌ | ⚠️ | [details] |
| ... | ... | ... | ... | ... |
### Summary
- **Passed**: N/M specs
- **Failed**: [list]
- **New issues**: [list]Step 5: Offer Actions
If failures are found:
- Investigate — deep dive into specific failures
- Compare with main — check if failures are regressions
- File issues — create GitHub issues for new failures
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.

