Cynefin Classification
SkillDev toolsWhen the right response mode is unclear, classify the cause-effect domain first; decompose disorder.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Cynefin Classification skill
What this skill tells your AI
The instructions your AI receives, as published by tjboudreaux/cc-thinking-skills in skills/thinking-cynefin/SKILL.md and read by ahel’s review.
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>
Verification
- Falsify: Reliable prediction → not Complex. No safe probe → Chaotic. Working runbook → Clear.
- Stop: Stop classifying once the matched action is clear.
- Over-application guard: Do not call work Complex to avoid analysis or Complicated to avoid a standard fix. One label per unit; decompose multi-domain work.
Signals
- GitHub stars
- 1k
- Forks
- 158
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
thinking-cynefin- Source
- github.com/tjboudreaux/cc-thinking-skills