FitClaw Public Core
v1.0.0Public-safe FitClaw coaching workflow covering onboarding, hydration, nutrition, and training structure. Use when demonstrating a reusable AI fitness coachin...
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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 and description match the provided content: onboarding, hydration, nutrition, and training workflows. The skill requires no binaries, environment variables, installs, or config paths — which is appropriate for an instruction-only, public-safe packaging layer.
Instruction Scope
SKILL.md and the referenced module files stick to coaching workflows, guardrails, memory guidance, and a sanitization checklist. The instructions explicitly forbid including credentials, private user media, and private operational materials. There are no directives to read arbitrary system files, contact unknown endpoints, or exfiltrate data.
Install Mechanism
No install specification and no code files — lowest-risk pattern. Nothing is downloaded, extracted, or written to disk by the skill itself.
Credentials
The skill requests no environment variables, credentials, or config paths. The memory/storage guidance is generic and emphasizes public-safe handling; this is proportionate for a workflow-only skill.
Persistence & Privilege
always is false and the skill does not request persistent system presence or modification of other skills or system-wide settings. Autonomous invocation by the agent is allowed (platform default) but the skill does not request elevated privileges.
Assessment
This package appears coherent and low-risk as provided. Before publishing or deploying, manually follow the included 'public sanitization checklist': review examples and templates to ensure no private data or operational bindings slipped in; confirm any reminders/notifications you wire into a runtime use explicitly provisioned credentials and are limited to the expected service; and if you add runtime memory/storage or external integrations later, re-evaluate credentials and endpoints to ensure they match the skill's public scope. If you want higher assurance, have a reviewer verify that example data is anonymized and that no hidden endpoints or external call patterns were introduced when adapting this into a runnable 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.