Skill audit report
Safe-to-fail experiment for Complex domain problems where cause-effect is only visible in retrospect. Two-phase: foreground qualify → background probe → sense result. Use when: probe, safe-to-fail, test hypothesis, experiment with hypothesis, Complex domain with hypothesis. NOT for brainstorming (use brainstorm) or known cause-effect (use investigate).
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
FAIL30
20 GitHub stars
Stars/forks activity
FAIL32
20 stars, 7 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
27d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
INFO76
Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk
WARN54
command execution surface, credential or environment access
Install availability
PASS92
npx skills add digital-stoic-org/agent-skills --skill probe
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL36
secrets or environment access, shell or command execution
Repository evidence
PASS86
https://github.com/digital-stoic-org/agent-skills/tree/main/cognitive/skills/probe
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add digital-stoic-org/agent-skills --skill probe
Repository
88
https://github.com/digital-stoic-org/agent-skills/tree/main/cognitive/skills/probe
License
86
MIT
Maintenance
100
27d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
84
Usable description available
Dependency risk
54
command execution surface, credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
36
secrets or environment access, shell or command execution
Stars/forks activity
32
20 stars, 7 forks; issue activity unavailable in current metadata
Adoption
42
20 GitHub stars
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Warnings
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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