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Thinking Kepner Tregoe

CommunityPopular
tjboudreaux
thinking-kepner-tregoe

Use when a selective defect needs IS/IS-NOT difference analysis or a consequential option choice needs must/want weighting and adverse-consequence comparison.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-kepner-tregoe
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 Kepner Tregoe 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-kepner-tregoe .claude/skills/thinking-kepner-tregoe
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Kepner Tregoe 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 Kepner Tregoe 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 Kepner Tregoe 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.

Kepner-Tregoe Analysis

Core rule: Diagnose deviations by testing causes against both IS and IS-NOT. Compare consequential choices by screening MUSTs, weighting WANTs, and exposing adverse consequences before selecting.

When to Use

  • A defect affects some objects, places, times, or cohorts but not comparable others.
  • Several candidate causes remain and the contrast boundary can discriminate them.
  • A consequential option choice has explicit non-negotiables, competing objectives, and risks that should be compared consistently.

When NOT to Use

  • A uniform failure has no meaningful IS-NOT contrast, or the cause is already confirmed.
  • One cheap observation settles the cause or one option plainly dominates every requirement.
  • The criteria cannot be made operational; clarify them before assigning weights.
  • The task is forward failure discovery for a planned change rather than diagnosis or option selection.

Procedure

  1. Choose the mode. Use Problem Analysis for a deviation from expected behavior; use Decision Analysis for a choice among options. State the target and do not mix scores with causal evidence.
  2. Frame the target. For a deviation, record object, defect, location, time, extent, and impact. For a choice, state the decision, alternatives, constraints, and deadline.
  3. Problem Analysis — build IS/IS-NOT. For WHAT, WHERE, WHEN, and EXTENT, record IS, closest comparable IS-NOT, and the distinction unique to the IS side. List changes near the first occurrence.
  4. Problem Analysis — difference-test causes. Generate candidates from distinctions and changes. A candidate survives only if it explains both IS and IS-NOT. Run the cheapest discriminating check; stop when one verified cause explains the full boundary.
  5. Decision Analysis — screen and score. Define pass/fail MUSTs and weighted WANTs (1–10 importance) before scoring. Eliminate options that fail any MUST; score survivors against each WANT and calculate weighted totals using the same scale.
  6. Decision Analysis — test downside and sensitivity. For leading options, list adverse consequences with probability × impact and identify assumptions or weight changes that would reverse the ranking. Do not let a high total conceal a ruinous failure mode.
  7. Decide or expose the gap. Return the verified cause or highest-ranked acceptable option, the evidence/score behind it, residual risk, and next verification. If no cause verifies or no option passes MUSTs, return open/none rather than force a winner.

Output

Return one mode-specific decision artifact:

  • Problem Analysis: problem statement; IS/IS-NOT matrix with distinctions; nearby changes; candidate-vs-boundary tests; confirmed cause or next discriminating check.
  • Decision Analysis: decision statement; alternatives; MUST screen; weighted WANT matrix; adverse-consequence table; sensitivity/reversal conditions; selected option or none.

Verification

  • Falsify/stop: reject a cause that cannot explain both sides of the boundary. Reject a choice if it fails a MUST, depends on inconsistent scoring, or loses under a plausible weight/risk change that was hidden.
  • Over-application guard: skip the full matrix for an obvious cause, trivial choice, or one-shot check. Stop when the cause verifies or the option is robust enough for the stated stakes; extra rows are ceremony.

Frequently asked questions

What does the Thinking Kepner Tregoe AI skill do?

Use when a selective defect needs IS/IS-NOT difference analysis or a consequential option choice needs must/want weighting and adverse-consequence comparison.

Why use Thinking Kepner Tregoe on TypingMind?

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

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

Which AI models can use Thinking Kepner Tregoe?

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 Kepner Tregoe?

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

Is the Thinking Kepner Tregoe 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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