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Thinking Bounded Rationality

CommunityPopular
tjboudreaux
thinking-bounded-rationality

Use when search or investigation could run forever. Set an explicit good-enough threshold first, then stop at the first option that clears it.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-bounded-rationality
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 Bounded Rationality 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-bounded-rationality .claude/skills/thinking-bounded-rationality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Bounded Rationality 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 Bounded Rationality 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 Bounded Rationality 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.

Bounded Rationality

Under finite tool, context, and time budgets, stop at the first option that meets a predeclared aspiration level. Optimize only when the good-to-best gap is worth the remaining budget.

When to Use

  • Search or option comparison has no natural endpoint and could consume the turn budget.
  • Multiple options would clear the requirement and further comparison has diminishing returns.
  • The decision is reversible or low-stakes relative to more search.
  • You are gathering context beyond what the decision needs to ship.

When NOT to Use

  • Irreversible or high-stakes choices (data loss, security, migrations, public commitments) where the good-to-best gap is material.
  • Correctness gates: tests, security checks, and "did the fix work?" need the right answer, not a sufficient-looking one.
  • One cheap lookup would settle the fact — do it; do not satisfice past it.
  • The aspiration level cannot be stated — clarify the requirement first.

Procedure

  1. State decision and budget. Name the choice, residual tool/context/time budget, and reversibility.
  2. Set aspiration before searching. Write concrete pass/fail criteria for "good enough." Do not evaluate until the threshold is explicit.
  3. Search sequentially. Score options in encounter order against the threshold only. Skip full matrices unless step 1 marked high-stakes/irreversible.
  4. Stop at first adequate. When an option clears every criterion, select it immediately.
  5. Handle search failure without moving the goalposts. If nothing clears after the pre-set cap, preserve the threshold and report no adequate option. Relax only a criterion predeclared as non-load-bearing, record the relaxation, and resume within a new cap; never raise the bar after failure.
  6. Commit. Record choice and residual uncertainty; spend remaining budget on execution, not re-ranking.

Stop condition: First option meets the predeclared aspiration level, or the search cap is exhausted with none adequate.

Output

text
Decision: <choice>
Aspiration: <pass/fail criteria>
Search: evaluated N; stopped at first adequate | cap exhausted
Selected: <option or none>
Residual risk: <what further search might change>
Next spend: <execution step>

Verification

  • Falsify if you kept comparing after an option cleared the threshold, or invented the threshold after seeing winners.
  • Falsify if a correctness gate or irreversible decision was treated as satisficeable.
  • Over-application guard: if one cheap check settles a fact, look it up — do not invoke this skill.

Frequently asked questions

What does the Thinking Bounded Rationality AI skill do?

Use when search or investigation could run forever. Set an explicit good-enough threshold first, then stop at the first option that clears it.

Why use Thinking Bounded Rationality on TypingMind?

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

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

Which AI models can use Thinking Bounded Rationality?

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 Bounded Rationality?

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

Is the Thinking Bounded Rationality 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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