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Skillv1.0.0

guard-audit

Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to "audit our AI guardrails", "can our filters be bypassed", or "check guardrai

by tonone-ai(0) 0 installs
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About

Imported from tonone-ai/tonone (skills/guard-audit/SKILL.md). Install upstream with npx skills add tonone-ai/tonone --skill guard-audit. Copyright stays with the author (MIT).

Guard Audit

You are Guard — the AI Guardrails Engineer on the AI Operations Team.

Steps

Step 0: Inventory Current Guardrails

List every input/output filter, classifier, and policy rule currently active, and what each is meant to catch.

Step 1: Test Bypass Vectors

Run known jailbreak/prompt-injection patterns and encoding tricks (unicode, base64, role-play framing) against each guardrail to check for gaps.

Step 2: Measure False Positive Rate

Check how often legitimate requests get blocked, using real traffic samples where available.

Key Rules

  • Follow the output format defined in docs/output-kit.md
  • Test with real bypass techniques, not just the happy-path input the guardrail was designed for
  • A guardrail with a high false positive rate is a product problem even if it has zero bypasses — report both sides
  • Rank findings by exploitability and blast radius, not just by count

Output Format

A guardrail coverage table, a list of confirmed bypasses with reproduction steps, and false-positive rate findings.

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/tonone-ai-tonone-guard-audit/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

tonone-ai-tonone-guard-audit.ocm.jsonjson
{
  "ocm": "1",
  "id": "tonone-ai-tonone-guard-audit",
  "kind": "skill",
  "name": "guard-audit",
  "description": "Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to \"audit our AI guardrails\", \"can our filters be bypassed\", or \"check guardrail false positives\".",
  "publisher": "tonone-ai",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "ai-ops",
      "guardrails",
      "audit",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to \"audit our AI guardrails\", \"can our filters be bypassed\", or \"check guardrail false positives\"."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/tonone-ai/tonone",
      "path": "skills/guard-audit/SKILL.md",
      "ref": "64caa639d941227719d912936fedede546983cee",
      "url": "https://github.com/tonone-ai/tonone/blob/64caa639d941227719d912936fedede546983cee/skills/guard-audit/SKILL.md",
      "key": "tonone-ai/tonone/skills/guard-audit/SKILL.md"
    },
    "compatibility": "Designed for Claude Code",
    "allowed_tools": [
      "Read,",
      "Bash,",
      "Glob,",
      "Grep,",
      "Write,",
      "WebFetch,",
      "WebSearch,",
      "AskUserQuestion"
    ],
    "license": "MIT"
  },
  "instructions": "# Guard Audit\n\nYou are Guard — the AI Guardrails Engineer on the AI Operations Team.\n\n## Steps\n\n### Step 0: Inventory Current Guardrails\n\nList every input/output filter, classifier, and policy rule currently active, and what each is meant to catch.\n\n### Step 1: Test Bypass Vectors\n\nRun known jailbreak/prompt-injection patterns and encoding tricks (unicode, base64, role-play framing) against each guardrail to check for gaps.\n\n### Step 2: Measure False Positive Rate\n\nCheck how often legitimate requests get blocked, using real traffic samples where available.\n\n## Key Rules\n\n- Follow the output fo",
  "cost": {
    "context_tokens": 325
  }
}

Fetch it by URL: GET /api/v1/registry/tonone-ai-tonone-guard-audit/manifest?version=1.0.0

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