Claude Code Thinking Skills is a catalogue of 28 portable skills that give coding agents structured procedures for reasoning about decisions, diagnosis, risk, strategy, and related problems. It is intended for Claude Code, GitHub Copilot, Codex, Cursor, and other tools that support Agent Skills.
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 tjboudreaux/cc-thinking-skills --skill thinking-red-teamgit clone --depth 1 https://github.com/tjboudreaux/cc-thinking-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/tjboudreaux/cc-thinking-skills/thinking-red-team)<a href="https://agentmods.dev/skills/tjboudreaux/cc-thinking-skills/thinking-red-team"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-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/tjboudreaux/cc-thinking-skills/thinking-red-team"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-red-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00041 | $0.00893 |
| Opus 5 | $0.00020 | $0.00447 |
| Sonnet 5 | $0.00008 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
thinking-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 11d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red Team
Adversarial security review of systems you are authorized to assess. Attack before an outsider does, but report only what you can actually break: every finding needs a concrete exploit path and a check that the proposed fix closes it.
When to Use
- Security review of code, authentication, authorization, APIs, data handling, or infrastructure you control and are permitted to probe.
- Pre-launch hardening of systems that handle auth, money, personal data, or privileged actions.
- Checking whether a specific vulnerability class (injection, XSS, IDOR, auth bypass, SSRF, secret exposure, etc.) is present with a real path.
- Validating that a claimed control actually blocks the attack, not only that a scanner is quiet.
When NOT to Use
- No authorization to attack the target — stop; do not probe systems you do not own or have written leave to test.
- Speculative "best practice" notes without a reproducible exploit path — drop them; they are not findings.
- Plan, strategy, or decision stress-testing — use pre-mortem (how the plan fails) or steel-manning (strongest case against the decision).
- Architecture resilience without a security objective — use systems or pre-mortem.
- Scanner output alone as a report — patterns are leads; red-team requires an exploit path.
- Non-security root-cause or hypothesis localization — use scientific-method or five-whys-plus.
Procedure
- Confirm authorization and objective. State target, allowed scope, out-of-scope assets, success condition (e.g., unauthorized data read, privilege escalation), and stop rules. Refuse or narrow if authorization is unclear.
- Build the threat model. Name adversary profiles (anonymous external, authenticated user, privileged insider) and their goals under realistic access. Attacks without an actor and goal are noise.
- Map the attack surface. Enumerate entry points and trust boundaries: public endpoints, auth flows, APIs, uploads, admin surfaces, jobs, webhooks, secrets, and data stores. Note exposure and required privileges.
- Trace exploit paths. For each high-value surface, attempt concrete abuse: input manipulation, authz gaps, token/session misuse, injection, SSRF, IDOR, mass assignment, rate-limit bypass, secret leakage. Record exact steps and observed behavior.
- Apply the anti-fabrication gate. Keep a finding only if you can complete: entry point → ordered steps → realized impact on this code/config. Incomplete paths are dropped, not listed as "informational."
- Score severity and attempt defense bypass. Rate impact and exploitability. For each relevant control (rate limit, validation, session check), try a realistic bypass and record held vs broken.
- Prescribe and verify mitigations. For each kept finding, give a minimal concrete fix and state how to re-test that the path is closed. Prefer fixes that remove the exploit precondition. Stop when in-scope surfaces are covered or authorization/budget ends; zero findings is valid.
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.
- 11d ago First seen · 59 lines · 41 tokens per session scan A f1654bd34e07
thinking-red-team is a skill published in the GitHub repository tjboudreaux/cc-thinking-skills (1,300 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 893 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.
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