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 seb1n/awesome-ai-agent-skills --skill agent-red-teaminggit clone --depth 1 https://github.com/seb1n/awesome-ai-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/seb1n/awesome-ai-agent-skills/agent-red-teaming)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/agent-red-teaming"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/agent-red-teaming/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/seb1n/awesome-ai-agent-skills/agent-red-teaming"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/agent-red-teaming.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 | $0.00082 | $0.02062 |
| Opus 5 | $0.00041 | $0.01031 |
| Sonnet 5 | $0.00016 | $0.00412 |
| Haiku 4.5 | $0.00008 | $0.00206 |
Grade A, and why
agent-red-teaming 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Red Teaming
Find exploitable control failures without creating uncontrolled harm. Treat written authorization and rules of engagement as prerequisites for execution, not paperwork to complete afterward.
Inputs
Collect:
- Named target owner and explicit authorization for the exact systems to be tested
- Target identifiers, environment, accounts, endpoints, models, versions, and a reproducible configuration digest
- Start/end time, tester identities, source addresses, rate and cost limits, and emergency contact
- In-scope objectives and out-of-scope systems, tenants, data, techniques, and effects
- Agent architecture, tools, privileges, memory, retrieval, handoffs, identities, and external integrations
- Protected assets, security requirements, prior incidents, existing controls, and expected benign tasks
- Approved synthetic data, canary values, test destinations, cleanup plan, and evidence-handling rules
If target-specific authorization or scope is missing, stop at a non-executable assessment plan. Do not probe a live target to infer scope.
Output contract
Deliver:
- Signed-off or explicitly pending rules of engagement with scope, constraints, stop conditions, contacts, and cleanup duties
- A system and privilege map plus prioritized threat hypotheses
- A machine-readable, owner-approved campaign plan with unique case IDs, targets, environment, configuration digest, tester subjects, authorization reference, time window, limits, stop conditions, cleanup duties, protected invariants, and safe oracles
- Execution records tied to an approved case ID and unique test ID, with matching target/configuration, timestamps, observed limits, structured evidence, and cleanup traceability
- Deduplicated findings with reproducibility, evidence, impact, likelihood, preconditions, root control failure, and remediation
- Plan-denominator campaign metrics that distinguish passes, failures, blocked cases, errors, missing records, and tests not run
- Retest results, residual risk, cleanup confirmation, and any incident or scope deviation
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 136 lines · 82 tokens per session scan A a24670b11acd
agent-red-teaming is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 2,062 once invoked, about $0.0004 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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