Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vaquarkhan/platform-engineering-agent-skills --skill ai-safety-red-teaming-and-compliancegit clone --depth 1 https://github.com/vaquarkhan/platform-engineering-agent-skillsWrote 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/vaquarkhan/platform-engineering-agent-skills/ai-safety-red-teaming-and-compliance)<a href="https://agentmods.dev/skills/vaquarkhan/platform-engineering-agent-skills/ai-safety-red-teaming-and-compliance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/ai-safety-red-teaming-and-compliance/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/vaquarkhan/platform-engineering-agent-skills/ai-safety-red-teaming-and-compliance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/ai-safety-red-teaming-and-compliance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00070 | $0.00766 |
| Opus 5 | $0.00035 | $0.00383 |
| Sonnet 5 | $0.00014 | $0.00153 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
ai-safety-red-teaming-and-compliance 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Safety, Red-Teaming & Compliance
Core Competencies & Directives
Hardcode defenses against the OWASP Top 10 for Agentic Applications 2026. Explicitly mitigate Agent Goal Hijack (ASI01:2026), Tool Misuse & Exploitation (ASI02:2026), and Memory & Context Poisoning (ASI06:2026).
Structure all AI deployments to align with the NIST AI RMF (Govern, Measure, Manage) and the EU AI Act's high-risk compliance mandates (effective August 2026).
When writing CI/CD pipelines for AI applications, integrate automated red-teaming checks using frameworks like Confident AI (via pytest) or DeepTeam for multi-turn adversarial testing.
Enforce defense-in-depth for AI agents: apply the principle of least privilege, execute agent actions in isolated sandboxes, and implement human oversight loops for high-stakes tool calls.
When to Use
- designing agent tool permissions and sandbox boundaries
- adding red-team pytest jobs to CI/CD
- mapping controls to NIST AI RMF or EU AI Act high-risk requirements
- mitigating ASI01, ASI02, or ASI06 threat classes
- implementing human-in-the-loop for destructive or privileged tool calls
Workflow
- Threat model — use
references/owasp-asi-agentic-guardrails.mdfor ASI01/02/06 controls. - Govern (NIST) — document purpose, data lineage, and approval owners in
templates/ai-system-card.yaml. - Measure — wire red-team CI from
templates/ai-redteam-pytest.yaml(Confident AI pytest or DeepTeam multi-turn). - Manage — least-privilege tool scopes, sandbox runtime, escalation to human approval for high-stakes actions.
- Validate — block
/shipuntil red-team suite passes and oversight hooks are configured.
ASI Control Matrix
| Risk | Required control |
|---|---|
| ASI01 Goal Hijack | Immutable system prompts, goal integrity checks, instruction hierarchy |
| ASI02 Tool Misuse | Allowlisted tools, argument schema validation, rate limits |
| ASI06 Context Poisoning | Signed memory writes, retrieval provenance, TTL on agent memory |
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 · 66 lines · 70 tokens per session scan A bef73443ecea
ai-safety-red-teaming-and-compliance is a skill published in the GitHub repository vaquarkhan/platform-engineering-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 766 once invoked, about $0.0003 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 2026-08-31.
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