SJYKJ Test Case Generator
v1.0.0Automatically generate pytest test cases from Python code using five design methods including equivalence partitioning and boundary value analysis.
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included code: the package implements equivalence-class, boundary-value, scenario, cause-effect and error-guessing analyzers and generates pytest and Markdown outputs. No unrelated environment variables, binaries, or configuration paths are requested.
Instruction Scope
SKILL.md directs running python -m src.test_generator and src.requirement_analyzer which generate tests from local source or requirements docs. The code legitimately reads local files (source files, markdown, local data/error_patterns.json). Be aware CoverageAnalyzer uses subprocess to run 'python -m coverage' and 'pytest' — that actually executes tests (and thus executes the user's code). This runtime behavior is expected for a test generator but is the primary安全 surface to consider.
Install Mechanism
No install spec is provided (instruction-only entry), and all dependencies are local Python modules. The only external execution is via subprocess calling installed tools (python, pytest, coverage) — there is no remote download or archive extraction in the install step.
Credentials
No environment variables, credentials, or config paths are requested. The code only loads a local JSON error-pattern file and reads/writes files specified by the user; no secret exfiltration behavior or unrelated credential access is present.
Persistence & Privilege
The skill does not request permanent/always-enabled presence and does not modify other skills or global agent configuration. It runs as a normal user-space utility with no elevated privileges.
Assessment
This skill appears to be what it says: a local pytest test-case generator implemented in Python. Things to consider before installing/using: 1) Source provenance — the package has no homepage and an opaque owner ID; review the included source files if you need supply-chain assurance. 2) Execution risk — CoverageAnalyzer or running generated tests will execute the target Python code (and any side effects it contains). Only run tests against code you trust or inside an isolated environment (container/VM/virtualenv). 3) Tooling requirements — to run coverage features you need pytest and coverage installed; subprocess calls rely on those binaries being available. 4) Review generated tests before executing them if you want to avoid surprising side effects. If you want, I can point out specific lines that invoke subprocess/perform file I/O or produce an itemized list of functions that execute code so you can audit them quickly.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.