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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description, included guides, and the single declared dependency ('rich') match: all instructions and examples are about installing and using the Rich library and its components (Console, Table, Progress, Syntax, etc.). No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
The SKILL.md and guides instruct the agent to run typical local commands (python --version, pip install rich, python -m rich), create or edit local files, and modify application code to integrate Rich (replace print/pprint, install traceback/pretty). These are within scope, but some recommended actions can expose sensitive data if used carelessly — e.g., traceback.install(show_locals=True) and logging handlers that record locals can reveal secrets; writing logs/exported HTML/SVG to disk may persist output. The skill does not instruct any network exfiltration or access to unrelated system configuration.
Install Mechanism
This is an instruction-only skill (no install spec). Install guidance uses normal package managers (pip, conda, pip install git+https://github.com/Textualize/rich.git, uv add). No downloads from suspicious hosts or archive extraction instructions are present.
Credentials
The skill requests no environment variables or credentials. It suggests setting benign environment variables related to terminal color (FORCE_COLOR, TERM, NO_COLOR) and recommends virtual environments. Be aware that examples show writing logs/files (app.log, output.html/output.svg) and enabling show_locals for tracebacks — these could persist or display sensitive values if the program processes secrets.
Persistence & Privilege
always is false and the skill does not request permanent platform privileges or modify other skills. It is instruction-only and does not ask to store tokens or change agent-wide settings.
Assessment
This guide appears coherent and focused on teaching/using the Rich library. Before running anything the AI suggests: 1) Prefer installing into a virtual environment (venv/conda) rather than globally. 2) Review any pip/conda commands the agent will execute, and avoid running arbitrary code from unknown sources. 3) Be cautious enabling traceback.show_locals or installing pretty() globally — these can reveal local variables (including secrets) on exceptions or REPL sessions. 4) When logging to files or exporting HTML/SVG, remember outputs are persisted locally and may contain sensitive data. 5) If you want the AI to modify your project files, review diffs before applying changes. No credentials are requested by this skill.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