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Thinking Ooda

CommunityPopular
tjboudreaux
thinking-ooda

Use under time pressure when the situation is still changing and you must act before certainty — cycle Observe→Orient→Decide→Act on ~70% confidence, then re-observe.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-ooda
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Ooda AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-ooda .claude/skills/thinking-ooda
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Ooda in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thinking Ooda on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thinking Ooda is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OODA Loop

Core rule: For reversible moves under time pressure, act on ~70% confidence, then immediately re-observe. Cycle faster than the situation compounds; a late perfect plan loses to a fast loop.

When to Use

  • Incident response, outage, or ongoing degradation where state is still moving.
  • Debugging a moving target (intermittent failure, live traffic shift).
  • Any time-bounded decision where waiting for full certainty costs more than a reversible action.

When NOT to Use

  • The situation is static and you have time — deliberate analysis or a hypothesis differential wins.
  • The next action is irreversible or high blast-radius — raise the evidence bar; 70% is not enough.
  • You can cheaply localize the cause (read the failing diff, log, or metric) — test that hypothesis directly instead of looping in the dark.
  • There is no time pressure and no changing environment — OODA adds churn without value.

Procedure

  1. Observe (time-boxed): gather the cheapest high-signal state now — metrics, logs, alerts, recent deploys/config, and feedback from the last action. Cap the window; do not collect forever.
  2. Orient: match observations to a pattern and form ≥2 candidate explanations. Update or discard the mental model when data contradicts it; refuse single-hypothesis lock.
  3. Decide: pick one reversible action that tests the leading hypothesis. State confidence (~70% threshold for reversible moves), the predicted effect, the observation you will check next, and a time box for that check.
  4. Act: execute once, decisively, with a known rollback or degrade path.
  5. Re-observe immediately: compare outcome to prediction within the time box; feed the result into the next Observe. Loop until stable or until the next move is no longer reversible enough for this skill.
  6. Stop condition: exit the loop when the system is stable, the remaining work is static analysis, or the next step requires irreversible commitment — then switch method.

Output

A cycle record (repeat per loop):

  1. Observed — current signals and what changed since last cycle.
  2. Orientation — ≥2 hypotheses; which one leads and why.
  3. Decision — action, confidence, predicted effect, next observation, time box.
  4. Act + result — what ran and what the immediate re-observe showed.
  5. Loop status — continue / stable / escalate out of OODA.

Verification

  • Falsify/stop: if you cannot name a reversible next action and a near-term observation that would refute it, stop looping and gather more evidence or escalate. If re-observe never happens after act, the loop is broken — fix that before another action.
  • Over-application guard: do not OODA static design work, irreversible launches, or cases where a single cheap localization check ends the uncertainty. Do not wait for 100% confidence on reversible mitigations under active incident pressure.

Frequently asked questions

What does the Thinking Ooda AI skill do?

Use under time pressure when the situation is still changing and you must act before certainty — cycle Observe→Orient→Decide→Act on ~70% confidence, then re-observe.

Why use Thinking Ooda on TypingMind?

Because you install it once and use it with any model. Thinking Ooda is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thinking Ooda in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-ooda. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Ooda?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thinking Ooda?

As many as you like. As long as a model supports skills, you can use Thinking Ooda with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thinking Ooda AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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