Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mtarcure/claude-vibe-squad/main/.agents/skills/agentic-safety-audit/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mtarcure/claude-vibe-squad/agentic-safety-audit)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/agentic-safety-audit"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/agentic-safety-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/agentic-safety-audit"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/agentic-safety-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00094 | $0.00618 |
| Opus 5 | $0.00047 | $0.00309 |
| Sonnet 5 | $0.00019 | $0.00124 |
| Haiku 4.5 | $0.00009 | $0.00062 |
Grade A, and why
agentic-safety-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Safety Audit
Audit an LLM agent system for the failure modes that only exist because a model is in the control loop.
Steps
- Map the trust boundary: which inputs reach the model, which of those are attacker-influenced (web pages, files, tool and parameter descriptions, tool output, other agents), and which model outputs become actions. Treat prose supplied by an MCP server or mutable tool registry as untrusted even when its JSON shape validates.
- Enumerate the action surface — every tool, shell, network call, file write, and spend the agent can reach, plus everything those actions can reach transitively.
- Test prompt injection at each untrusted input: can retrieved content, a tool/schema description, or tool output redirect the agent, expose protected context, or invoke an unintended action? Use a harmless scoped canary, never real secret material or a destructive command.
- Check the confused-deputy path: does the agent act with credentials or scope broader than the requester's own authority?
- Verify gates are enforced by the harness, not by instructions. An approval that the model can talk itself past is not a gate; test it with an adversarial prompt.
- Check scope containment: write scope, network scope, and working directory. Prove containment with a real denied attempt, not by reading the config.
- Audit memory and state: can untrusted content be written into durable memory and later recalled as if it were operator instruction? Check that recalled memory is labelled untrusted at the point of use.
- Check the multi-agent edges: a subagent's output re-entering a parent as trusted context is an injection path, and delegation frequently widens scope silently.
- Review failure behavior: on tool error, timeout, or refusal, does the agent stop, or does it improvise a less-safe path?
- Record every finding with the concrete prompt or input that triggers it.
Acceptance
- The trust boundary and full action surface are enumerated, including transitive reach.
- Injection was actually attempted at each untrusted input, with the payloads recorded.
- Every claimed gate was tested adversarially and observed to hold or fail.
- Containment claims rest on an observed denial, not on configuration text.
- Memory write-then-recall and agent-to-agent edges are covered explicitly.
- Tool/schema descriptions and externally controlled outputs are inventoried by provenance and mutability; stripping or quarantining their untrusted prose blocks the harmless canary.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 30 lines · 94 tokens per session scan A e07ae8d0a116
agentic-safety-audit is a skill published in the GitHub repository mtarcure/claude-vibe-squad (152 stars, last pushed 3d ago), licensed MIT. It adds 94 tokens to every session and 618 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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