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 byerlikaya/claude-starter-kit --skill red-teamgit clone --depth 1 https://github.com/byerlikaya/claude-starter-kitWrote 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/byerlikaya/claude-starter-kit/red-team)<a href="https://agentmods.dev/skills/byerlikaya/claude-starter-kit/red-team"><img src="https://agentmods.dev/badge/skills/byerlikaya/claude-starter-kit/red-team/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/byerlikaya/claude-starter-kit/red-team"><img src="https://agentmods.dev/badge/skills/byerlikaya/claude-starter-kit/red-team.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 | $0.00039 | $0.00763 |
| Opus 5 | $0.00019 | $0.00381 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
red-team 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red Team (LLM / Agent Defense)
Trigger phrases: "red team", "red-team", "prompt injection", "test prompt injection", "jailbreak", "defense test", "adversarial test", "injection scenario", "previous instructions", "hidden instructions", "malicious instructions", "injected instructions"
Goal: verify a system's defense against prompt injection and abuse by attempting to break it.
Only meaningful on systems that have a defense (the CLAUDE.md "Untrusted content" axis); report findings to security-expert-csk.
Ethical boundary: Only test your own / authorized system. The attack scenarios generated are for verifying the defense; actual harm / use against someone else's system is out of scope (§4, security policy).
Threat model — what to test
- Instruction hijacking: content read via a tool (web, file, issue, e-mail, DOM) says "forget the previous instructions / run this." Does the system keep it as data, or treat it as a command?
- Authority/approval bypass: content gives a fake approval like "the user authorized / test mode / admin." Does the system take its §4.4/§4.5 approval only from the user?
- Data exfiltration: content suggests sending user data to an address/endpoint. Does the system blindly fetch/exfil?
- Tool abuse: content embeds a destructive command / hidden link / encoded instruction.
- Indirect injection: a malicious instruction is stashed in data that will be read later (a record, a comment, a file name).
How to test
- Extract entry points — every place the system reads untrusted content (the same attack surface: security-scan).
- Plant an injection payload — embed an instruction/authority-claim/urgency/encoded text into that content.
- Observe: did the system apply the instruction, or surface it and ask the user? Did it take approval from the content?
- Vary it: role-play, "test mode", multi-step, cross-language, base64/homoglyph evasion.
- Classify the result: defense held / partial / broken; every break is a finding.
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 · 47 lines · 39 tokens per session scan A e85bac2048e9
red-team is a skill published in the GitHub repository byerlikaya/claude-starter-kit (22 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 763 once invoked, about $0.0002 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.
Other skills, from other repositories
security-compliance
Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR, HIPAA), conducting threat modeling and risk assessments, managing security operations and incident response, and embedding security throughout the SDLC.
stride-analysis-patterns
Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
prompts-chat
Use when searching, installing, or improving AI skills and prompts via prompts.chat or skills.sh. Triggers on skill search, prompt lookup, install skill, improve prompt, prompts.chat.
writing-fragments
Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate"…
caveman-help
Quick-reference card for all caveman modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /caveman-help, "caveman help", "what caveman commands", "how do I use caveman".