Skill audit report
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.
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
1d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO72
credential or environment access
Install availability
PASS92
npx skills add NoorQureshi/SploitAgent --skill ai-jailbreak
Install command safety
PASS92
standard package or runtime install path
Permission surface
INFO74
secrets or environment access
Repository evidence
PASS86
https://github.com/NoorQureshi/SploitAgent/tree/main/skills/ai-ml/ai-jailbreak
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add NoorQureshi/SploitAgent --skill ai-jailbreak
Repository
88
https://github.com/NoorQureshi/SploitAgent/tree/main/skills/ai-ml/ai-jailbreak
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
72
credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
74
secrets or environment access
Stars/forks activity
32
20 stars, 7 forks; issue activity unavailable in current metadata
Adoption
42
20 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.
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Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
51 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.
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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