test-generator
Skill8 starsChanged 3 months agoMITView on GitHub
Generates automated tests from an existing SDD spec. Maps each acceptance criterion to one or more test cases. Use after a spec is approved and before implementing the code it describes.
25 other projects use a version of this.
Adopted by
Most recently taken up by
- u9401066/zotero-keepertheir own version, first seen Nov 8, 2026
- Data-Wise/crafttheir own version, first seen Oct 31, 2026
- u9401066/pubmed-search-mcptheir own version, first seen Oct 20, 2026
- aidankaokao/claude-skillstheir own version, first seen Oct 9, 2026
- jablonkai/skillstheir own version, first seen Oct 1, 2026
- jcarlos78/GAIDEword for word, first seen Aug 19, 2026
Used with
Projects with it also use refactor-advisor, log-analyzer and 2 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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Also in 3 projects that use this
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Other versions25 projects use a version of this.
Word for word · 1
- jcarlos78/GAIDE★ 8100% same
Edited · 0
None found.
Wrote their own version · 24
- laolaoshiren/claude-code-skills-zh★ 907same name
- u9401066/pubmed-search-mcp★ 28same name
- u9401066/zotero-keeper★ 7same name
- bestdeejay-design/agent-skills★ 6same name
- Data-Wise/craft★ 4same name
- u9401066/nsforge-mcp★ 4same name
- Nembie/claude-code-skills★ 3same name
- suxiao1824308603/Qoder-devtools-skills★ 3same name
- u9401066/template-is-all-you-need★ 3same name
- Dokkabei97/forged-claude-code★ 2same name
14 more not listed
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 repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .claude/CLAUDE.mdCLAUDE.md
- adr-writerSkill
- code-reviewerSkill
- security-reviewerSkill
- spec-writerSkill
- verifierSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 14 edited versions we read keep these.
4
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 28
Check AGENTS.md or package.json / pyproject.toml to identify the test framework in use.
- Distinctive: line 54
Before finishing, produce a traceability table:
- Distinctive: line 61
If a criterion has no matching test → report the gap to the user.
- Distinctive: line 65
Each test proves one criterion: Don't bundle multiple criteria in one test.
- Distinctive: line 66
Readable names: Test names become executable documentation.
“Common” is measured: kept by at least 6 in 10 of the 14 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
<!-- GENERATED from agents/skills/test-generator.md by scripts/sync-adapters.sh — edit the source and re-run; do not edit this file. -->Skill: test-generator
When to activate
- An approved spec exists at
specs/<feature>/spec.md - Implementation hasn't started (or is just starting)
- The user explicitly asks for tests generated from a spec
Why run this before implementation
The skill is designed for tests-first: tests derived from the spec represent the external contract the implementation must honor. Implementing first blurs the criterion.
Playbook
1. Load the spec
Read specs/<feature>/spec.md, especially:
- The "Use cases" section
- The "Acceptance criteria" section
- The "Out of scope" section (to generate negative tests where applicable)
- The "Security considerations" section (abuse cases → tests proving the abuse fails)
2. Load the test stack
Check AGENTS.md or package.json / pyproject.toml to identify the test framework in use. Do not introduce a new framework.
3. Map criterion → test
For each acceptance criterion, generate one or more tests:
- Happy path test (the scenario described in the main use case)
- Alternative path test (variations mentioned in the spec)
- Error test (violated preconditions, invalid inputs)
- Edge case test (numeric limits, empty strings, empty lists)
- Abuse case test (from Security considerations: the test performs the abuse — wrong user, tampered ID, expired token — and asserts it is denied; these prove what must NOT happen)
4. Test file structure
# Suggested layout (adapt to the framework):
describe/feature("<Feature name>")
describe/context("Criterion 1: <criterion text>")
test/it("given X when Y then Z")
test/it("edge: ...")
describe/context("Criterion 2: ...")
...5. Confirm coverage
Before finishing, produce a traceability table:
| Acceptance criterion | Generated test(s) |
| Criterion 1 | test_criterion_1_happy, test_criterion_1_edge |
| Criterion 2 | test_criterion_2_happy, test_criterion_2_error |
If a criterion has no matching test → report the gap to the user.
Principles
- Each test proves one criterion. Don't bundle multiple criteria in one test.
- Readable names. Test names become executable documentation.
- No production code. This skill produces tests that will fail until the implementation happens (red-green-refactor).
- No excessive mocks. Mock only what is outside the scope of the test; code under test should actually run.
Expected output
One or more test files in the project's conventional location (tests/, __tests__/, etc.) + a traceability report.
Expected status of the tests at the end: red (failing) — because the implementation doesn't exist yet. That's the point.
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.

