tjboudreaux/cc-thinking-skills

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.

First seen Mar 12, 2026

Installation

$ npx skills add tjboudreaux/cc-thinking-skills --skill thinking-kepner-tregoe

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1.3K
License LICENSE
Default branch main
Open issues 2
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,649 B
  • docs SUMMARY.md 188 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 360 installs

SKILL.md

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.