Skill audit report
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
51 GitHub stars
Stars/forks activity
WARN53
51 stars, 59 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
1d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO64
credential or environment access, network or browser surface
Install availability
PASS92
npx skills add gmh5225/awesome-skills --skill ai-llm-skills-guide
Install command safety
PASS92
standard package or runtime install path
Permission surface
WARN46
secrets or environment access, filesystem or document access
Repository evidence
PASS86
https://github.com/gmh5225/awesome-skills/tree/main/.claude/skills/ai-llm-skills
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add gmh5225/awesome-skills --skill ai-llm-skills-guide
Repository
88
https://github.com/gmh5225/awesome-skills/tree/main/.claude/skills/ai-llm-skills
License
86
MIT
Maintenance
100
1d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
64
credential or environment access, network or browser surface
Install command safety
92
standard package or runtime install path
Permission surface
46
secrets or environment access, filesystem or document access
Stars/forks activity
53
51 stars, 59 forks; issue activity unavailable in current metadata
Adoption
68
51 GitHub stars
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.
Compare nearby options
Bypass an LLM's safety/guardrails to make it produce restricted output or ignore its policy. Load when testing an AI product's content controls, "jailbreak", "guardrail bypass", refusal testing, or safety evals. Signals: a chatbot/assistant with a usage policy, refusals to test, content filters.
20 Stars · Audit report
Abuse an LLM agent's tools/functions — coerce it to call tools with attacker-chosen args for SSRF, RCE, data exfil, or privilege abuse. Load when the target is an agent with tools/ function-calling/plugins, MCP servers, code interpreters, or "the assistant can do X". Signals: function-calling, tool schemas, browse/email/query/exec tools, autonomous agents.
20 Stars · Audit report
Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.
20 Stars · Audit report