Data Science Cv Repro Lab
v1.9.0Public ClawHub skill for execution-grade CV experiments and evidence capture across Colab, Kaggle, browser automation, and GPU VMs.
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
Capability signals
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
OpenClaw
Benign
high confidencePurpose & Capability
The name/description promise (reproducible CV evidence capture across Colab, Kaggle, browser automation, and GPU VMs) matches the included artifacts: run-card and manifest initializers, a context capture script, browser-run helpers, and many references describing safe workflows. The only required runtime item is Python (python3/python), which is appropriate for the bundled scripts.
Instruction Scope
SKILL.md instructs the agent to run the bundled Python helpers to create manifests and capture run context (git state, module versions, GPU snapshot, etc.). The scripts call local commands (git, nvidia-smi) and write sanitized JSON/markdown outputs. They do not issue network requests or attempt to read unrelated credentials. Note: scripts run git and nvidia-smi and will capture repo-relative paths, git status, module versions, and GPU info as evidence—this is expected but you should be aware those local details will be recorded in output files.
Install Mechanism
There is no install spec (instruction-only runtime plus Python scripts). Nothing is downloaded from external URLs or installed automatically; risk from install mechanism is minimal.
Credentials
The skill declares no required environment variables or credentials. The scripts include sanitization helpers that intentionally redact absolute paths and credential-like tokens when writing durable artifacts. No unexpected env vars or API keys are required.
Persistence & Privilege
Skill is not force-included (always:false) and is user-invocable. It does not request to modify other skills or global agent settings. Runtime behavior is limited to running the provided Python scripts when invoked.
Assessment
This skill appears internally consistent and focused on reproducible CV evidence capture. Before running it: (1) Inspect the JSON outputs it will create — they include git status, repo-relative paths, Python executable, module versions, and GPU info; do not publish those outputs if they contain sensitive filenames or identifiers. (2) Run the scripts in a controlled environment (local dev machine or throwaway VM) if you are uncertain about exposing repo details. (3) If you intend to publish run artifacts, review for any accidental secrets (filenames, dataset IDs, private notebook URLs) despite the built-in sanitizers. (4) If you want deeper assurance, request the omitted files (the truncated ones) for review or run the scripts with dry-run output paths to inspect what would be written.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
Any binpython3, python