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Thinking Effectuation

CommunityPopular
tjboudreaux
thinking-effectuation

Under genuine uncertainty with no reliable forecast, inventory means, cap downside at affordable loss, act for commitments, and let goals emerge from controllable moves.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-effectuation
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 Effectuation 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-effectuation .claude/skills/thinking-effectuation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Effectuation 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 Effectuation 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 Effectuation 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.

Effectuation

Under Knightian uncertainty, start from available means and affordable loss—not a fixed goal and predicted return. Create the path through controllable action and real commitments.

When to Use

  • The market, technology, or problem is novel enough that outcome probabilities are not trustworthy.
  • Means are clearer than the goal: identity, skills, assets, and network exist before a fixed target does.
  • A small action can buy information or a partner commitment without risking ruin.
  • Plans keep breaking because the environment shifts faster than forecasts.

When NOT to Use

  • The path is predictable: known market, knowable unit economics, established playbook → use causal planning.
  • A single wrong step is ruinous or irreversible → de-risk first; do not treat affordable-loss steps as free.
  • The goal is already fixed and resources are the only uncertainty → plan to the goal.
  • Routine execution with settled requirements → act; do not re-inventory means.

Procedure

  1. Inventory means only. List who you are (skills, constraints, values), what you know (domain, tools, data), and who you know (reachable partners, users, resources). Reject "what would be needed for an ideal plan" as input.
  2. Cap affordable loss. State the maximum time, money, reputation, and opportunity cost you can lose and still continue. If the proposed step exceeds any cap, redesign the step smaller or stop.
  3. Choose one controllable next action. Prefer the smallest action that can yield either (a) a real commitment from someone else or (b) discriminating information. Act inside the loss cap; do not optimize expected return.
  4. Seek commitments, not opinions. Share the working means-based offer. Anyone who commits resources, access, or work becomes a co-creator and may reshape the goal. Discard non-committing feedback as non-binding.
  5. Leverage contingencies. Treat surprises as new means: failed hires, competitor moves, off-roadmap requests. Ask "how can this expand control?" not "how do we restore the old plan?"
  6. Update means and goal, then stop or loop. Fold new means and commitments into the inventory; restate the emerging goal. Stop when a viable path is controlled enough to execute, the loss cap is exhausted without traction, or uncertainty collapses into a predictable plan (then switch to causal planning).

Output

Emit an effectuation brief:

  • means: identity / knowledge / network actually available now
  • affordable_loss: hard caps (time, money, reputation, opportunity)
  • next_action: one controllable step inside those caps
  • commitments_sought_or_won: who must put skin in the game, and what changed if they did
  • contingencies_used: surprises turned into means (or none)
  • emerging_goal: current goal shaped by means and commitments (may differ from the starting wish)
  • stop_or_loop: continue under effectuation, switch to causal planning, or halt

Verification

  • Falsify means-first claims: if the brief starts from a fixed goal and backfills resources, rewrite from means or abandon effectuation.
  • Loss-cap check: every recommended action must fit inside stated affordable loss; if not, shrink or stop.
  • Commitment test: progress that depends only on predictions or uncommitted interest is invalid—require at least one real commitment or a cheap information gain.
  • Over-application guard: if a reliable forecast and known playbook exist, do not force effectuation; plan causally.
  • Stop: after one means → loss-cap → action → commitment cycle with an explicit stop/loop decision; do not endless-explore under the effectuation label.

Frequently asked questions

What does the Thinking Effectuation AI skill do?

Under genuine uncertainty with no reliable forecast, inventory means, cap downside at affordable loss, act for commitments, and let goals emerge from controllable moves.

Why use Thinking Effectuation on TypingMind?

Because you install it once and use it with any model. Thinking Effectuation 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 Effectuation in TypingMind?

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

Which AI models can use Thinking Effectuation?

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 Effectuation?

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

Is the Thinking Effectuation 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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