Authorship CRediT Gen
v1.0.0Use when determining author order on research manuscripts, assigning CRediT contributor roles for transparency, documenting individual contributions to colla...
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byAIpoch@aipoch-ai
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
medium confidencePurpose & Capability
The code (scripts/main.py) implements generation of CRediT author contribution statements in text/json/xml and matches the skill's stated purpose. However, SKILL.md/Quick Start references an additional module (scripts.authorship_credit) and class names that do not exist in the file manifest — this is a documentation/code mismatch that reduces confidence in the packaging.
Instruction Scope
Runtime instructions are limited to validating inputs and running the packaged script. They do not request environment secrets, nor do they instruct network calls or broad system file reads. Concerns: SKILL.md suggests editing an in-file CONFIG block and imports from a non-existent module; the docs also contain duplicated/repeated description text, indicating sloppy documentation that could mislead users about what the tool will do.
Install Mechanism
No install spec is provided and there are no download or external install steps. This instruction-only packaging (plus a contained script) minimizes installation risk.
Credentials
The skill declares no required environment variables, no credentials, and no config paths. The code shown does not access env vars or external credentials.
Persistence & Privilege
The skill is not always-enabled and does not request elevated persistence. It does not modify other skills or system-wide settings according to the provided files.
Assessment
This skill appears to do what it says (generate CRediT / authorship statements) and does not request credentials or install external code, but the documentation is inconsistent: SKILL.md's Quick Start references scripts.authorship_credit and an in-file CONFIG that are not present. Before using it: (1) open and review the end of scripts/main.py to confirm there are no hidden network calls or writes to unexpected locations; (2) run python -m py_compile scripts/main.py and python scripts/main.py --help in a safe environment; (3) test using the provided example_authors.json to confirm behavior; (4) be cautious with real data — author names, affiliations, and contributions are personal data (sensitive), so avoid uploading or sharing them where they could be exposed. If you plan to rely on this skill in production, ask the publisher to fix the documentation mismatch (remove/replace references to missing modules and any non-existent CONFIG guidance) and to confirm the intended public API/CLI.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.