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-cynefingit 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-cynefin)<a href="https://agentmods.dev/skills/tjboudreaux/cc-thinking-skills/thinking-cynefin"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-cynefin/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-cynefin"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-cynefin.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 | $0.00024 | $0.00603 |
| Opus 5 | $0.00012 | $0.00302 |
| Sonnet 5 | $0.00005 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
thinking-cynefin 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cynefin Classification
Classify by cause-effect, then use only the matching response mode. Wrong-domain method is the failure mode.
When to Use
- Unsure whether to run a playbook, analyze, probe, or stabilize.
- A method keeps failing and domain mismatch is plausible.
- A novel or mixed problem needs approach selection before solution work.
When NOT to Use
- Domain and method are already agreed—execute.
- Task is finding a specific cause, not choosing an approach.
- Classification done; switch to the domain method—do not re-label endlessly.
- Pure mechanical edits with no approach uncertainty.
Procedure
- State the unit. Name the decision, incident, or subsystem; if mixed, list separable parts.
- Probe cause-effect. Is the link obvious, expert-analyzable, retrospective only, or imperceptible in turbulence? Check predictability, urgency, and probe safety.
- Assign one domain per unit:
- Clear — obvious → Sense → Categorize → Respond with a runbook.
- Complicated — expert-analyzable → Sense → Analyze → Respond; several valid answers.
- Complex — emergent → Probe → Sense → Respond with safe-to-fail probes; amplify/dampen signals.
- Chaotic — no safe sensing time → Act → Sense → Respond; stabilize first.
- Disorder — unknown → split and classify each part.
- Mismatch check. Reject Clear if its runbook fails; Complicated if analysis cannot predict; Complex if probing is unsafe; Chaotic once safe probing becomes possible.
- Commit and stop. Output domain + first actions. Re-classify only on evidence of domain shift.
Stop when every unit has one domain, first actions, and a falsifier—or disorder is decomposed.
Output
unit: <decision/incident/part>
domain: clear | complicated | complex | chaotic | disorder
evidence: <cause-effect basis>
response_mode: <Sense-Categorize-Respond | Sense-Analyze-Respond | Probe-Sense-Respond | Act-Sense-Respond | decompose>
first_actions: <1-3 concrete steps>
falsifier: <what forces reclassification>
parts: <only if disorder>
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 · 56 lines · 24 tokens per session scan A 65ffbfeadf13
thinking-cynefin is a skill published in the GitHub repository tjboudreaux/cc-thinking-skills (1,300 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 603 once invoked, about $0.0001 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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