Case Study Factory
v1.0.1把项目材料转成案例文章，提炼起点、动作、结果和复用价值。；use for case-study, content, storytelling workflows；do not use for 伪造客户背书, 公开敏感数据.
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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, SKILL.md, README, resources, and scripts all align: the skill converts project materials into structured case-study Markdown and can audit directories or CSVs. Required binary (python3) is appropriate and no unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md confines the skill to producing draft outputs and using local resources. The provided script reads files and directories the user supplies and performs pattern scans (e.g., searching for headings, CSV fields, or secret-like strings). This is appropriate for a content/audit tool, but it means the tool will read any path you pass it—do not point it at broad or sensitive locations (e.g., / or your whole home) unless you intend that.
Install Mechanism
No install spec is present (instruction-only with bundled script). That is low-risk: nothing is downloaded or installed from remote sources and the script relies only on the Python standard library.
Credentials
The skill requires no environment variables or credentials. The script does not reference external auth tokens or network endpoints; it only reads local files supplied by the user.
Persistence & Privilege
always is false and the skill is user-invocable. It does not request permanent presence or modify other skills or system-wide settings. Autonomous invocation is allowed by platform default but not inherently risky here given the limited footprint.
Assessment
This skill appears to do what it says: produce structured case-study drafts from user-provided inputs and optionally audit a project directory or CSV. Before running: (1) review the bundled scripts if you want extra assurance (they are included and use only the stdlib); (2) avoid passing broad or sensitive paths (do not point --input at /, your home, or system directories) because the script will read files you give it; (3) prefer using the examples or a sanitized copy of project materials; (4) use --dry-run or redirect output to files for review before taking any external actions; (5) if you enable autonomous agent invocation, be aware the agent could choose to run the local script on files it can access — restrict the skill to explicit user invocation if that concerns you.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.
Runtime requirements
🏭 Clawdis
OSmacOS · Linux · Windows
Binspython3