cynefin

cynefin is a skill for Claude Code, Codex from deciqAI/knowledge-skills. It costs 96 tokens per session (1,890 once invoked), scanned A, original, MIT.

A framework for choosing how to respond based on the kind of situation: routine, analyzable, emerging, chaotic, or not yet understood. Cynefin is pronounced “kuh-NEV-in” and is a Welsh word related to a place or habitat.

In plain words
What is it for?
It helps classify problems, decide whether to follow a procedure, analyze, experiment, act immediately, or first break an unclear situation into smaller parts.
Why use it?
It helps teams avoid applying a standard procedure or lengthy analysis when the situation is changing, uncertain, or urgent.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps classify problems, decide whether to follow a procedure, analyze, experiment, act immediately, or first break an unclear situation into smaller parts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/deciqai/knowledge-skills/cynefin
Install

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.

Any agent
npx skills add deciqAI/knowledge-skills --skill cynefin
Clone the repo
git clone --depth 1 https://github.com/deciqAI/knowledge-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cynefin

README.md
[![agentmods](https://agentmods.dev/badge/skills/deciqai/knowledge-skills/cynefin/github.svg)](https://agentmods.dev/skills/deciqai/knowledge-skills/cynefin)
Your own site
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/cynefin"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/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.

agentmods 80×15 button for cynefin

Your own site · 80×15
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/cynefin"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/cynefin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,890 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 18
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00096 $0.01890
Opus 5 $0.00048 $0.00945
Sonnet 5 $0.00019 $0.00378
Haiku 4.5 $0.00010 $0.00189

Measured 9d ago against content hash 1afcaf61f5f0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

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 9d 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.

cynefin/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cynefin

Overview

Cynefin (pronounced "kuh-NEV-in"; Welsh for "habitat") is a sense-making framework by Dave Snowden (IBM, 1999). Its claim: the right decision approach depends on which of five domains the situation falls into — Clear (obvious cause-effect, use SOP), Complicated (knowable with expertise, use analysis), Complex (emergent, probe first), Chaotic (absent cause-effect, act first), Confused (unknown domain, decompose first). The most common and costly error: treating Complex problems as Complicated.

Composes with ooda-loop, feedback-loops, antifragile, first-principles.

When to Use

  • A familiar approach has stopped working and you can't articulate why
  • Experts disagree on the right answer — a crisis unfolding where the previous playbook doesn't apply
  • "Best practices from X" imported without checking if the domain matches
  • A team is over-planning something emergent, or "let's get more data" when data won't come without action
  • Allocating AI capex or racing AI-native competition: deciding which AI bets are engineering (Complicated), emergent agent/adoption experiments (Complex), or live incidents (Chaotic)

Not when: domain is unambiguously Clear (execution only); small-stakes one-shot; specialized framework already fits.

Coaching Novices (Adaptive Front Door)

Engine mode: concrete case → run The Process. Coach mode: unfamiliar → guide step by step. In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

  1. Classify the situation (Clear/Complicated/Complex/Chaotic) and match decision method to domain.
  2. Check fit: if unambiguously routine (Clear), skip framework.
  3. Elicit their real case — decision, current method, cause-effect structure.

[WAIT — do not advance until user responds]

  1. Are cause-effect relationships obvious, knowable, emergent, or absent? Is current method matched?

[WAIT — do not advance until user responds]

  1. Close: named domain + matched decision method + boundary watch.

[WAIT — do not advance until user responds]

Read the full file on GitHub · 123 lines

Files

What ships with it

4 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.

Changes

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.

  1. 9d ago First seen · 123 lines · 96 tokens per session scan A 1afcaf61f5f0

Subscribe to this mod's changes

cynefin is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 7d ago), licensed MIT. It adds 96 tokens to every session and 1,890 once invoked, about $0.0005 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 2026-08-31.

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