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Thinking toolkit

Skill ponomr/thinking-toolkit

Select and apply a complete toolkit of decision-making, problem-solving, systems-thinking, and communication models. Use when a user explicitly requests a named model or needs help framing a problem, comparing options, setting priorities, examining consequences, finding causes, estimating unknown quantities, planning toward a distant goal, stress-testing a plan, understanding a system, resolving conflict, generating solutions, giving feedback, or structuring a message. Choose the smallest useful model set automatically when the user leaves the method open.From its SKILL.md

Install
npx -y skills add ponomr/thinking-toolkit

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SKILL.md

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Thinking Toolkit

Apply structured thinking without assuming access to tools, browsing, code, memory, or a particular LLM provider. Use plain language and produce artifacts that remain useful outside the conversation.

Core Contract

  1. Reply in the user's language. Keep standard model names recognizable.
  2. Preserve user agency. Treat model outputs as decision support, not automatic truth or authority.
  3. Separate observed facts, user-provided claims, assumptions, hypotheses, estimates, preferences, and recommendations.
  4. Never invent missing evidence. Mark unknowns and propose a way to resolve only the unknowns that could change the outcome.
  5. Give a concise selection rationale and the resulting artifact. Do not expose private hidden reasoning or produce a diary of internal deliberation.
  6. Match depth to stakes, reversibility, uncertainty, and user intent.

Choose the Mode

Explicit-model mode

Use the requested model when the user names it or an unambiguous alias. Read the catalog, then read only that model's card. Add a second model only when the user permits it and the first model leaves a distinct gap that materially affects the result.

Automatic-selection mode

Use this mode when the user describes a situation without naming a method.

  1. Identify the job: decide, prioritize, diagnose, reframe, generate, map a system, resolve conflict, give feedback, or communicate.
  2. Identify the dominant uncertainty: missing evidence, unclear values, multiple criteria, causal ambiguity, dynamics, time pressure, or audience.
  3. Read the catalog and shortlist the models whose selection cues match.
  4. Choose one primary model. Add at most two complementary models only when each has a separate role in a clear sequence.
  5. State the selected model or sequence and explain the choice in one or two sentences.

Use the Adaptive Workflow

1. Frame the situation

Capture only what matters:

  • the desired outcome and decision owner or audience;
  • scope, constraints, time horizon, and deadline;
  • available options, evidence, and prior actions;
  • stakes, reversibility, uncertainty, and affected people.

Ask up to three focused questions when missing information could materially change the model, framing, or recommendation. Otherwise proceed and label reasonable assumptions.

2. Set the working depth

  • Use a quick pass for low-stakes, reversible, time-sensitive situations.
  • Use a standard pass for ordinary planning, analysis, and communication.
  • Use a deep pass for consequential, hard-to-reverse, contested, or systemic situations. Include sensitivity checks, disconfirming evidence, and an exit or review condition.

3. Apply the model faithfully

Read the selected card before using it. Follow its procedure in order, adapt the questions to the user's context, and create the specified output. Do not reduce a model to a label or generic advice.

4. Test the result

Check for unsupported causal claims, hidden assumptions, omitted stakeholders, double-counted criteria, false precision, and missing alternatives. Where relevant, test how the result changes under a plausible alternative assumption.

5. Close with action

End with the decision, insight, draft, experiment, or next step the user asked for. State unresolved uncertainties and define what evidence or event should trigger a review.

Fast Selection Map

User needPrimary model
Examine a choice from distinct perspectivesSix Thinking Hats
Sort work by urgency and importanceEisenhower Matrix
Trace downstream consequencesSecond-Order Thinking
Compare options across weighted criteriaDecision Matrix
Prioritize by benefit and required workImpact-Effort Matrix
Check a conclusion for inferential leapsLadder of Inference
Match decision effort to stakes and comparabilityHard Choice Model
Decide and adapt under time pressureOODA Loop
Match action to the nature of a situationCynefin Framework
Balance product speed and quality using confidenceConfidence Determines Speed vs. Quality
Focus effort on the few contributors that drive most of an effectPareto Analysis
Plan backward from a defined desirable futureBackcasting
Organize possible causes of a defined effectIshikawa Diagram
Trace one incident to a process-level fixFive Whys
Estimate an unknown quantity without direct dataFermi Estimation
Challenge a plan from an adversary's perspectiveRed Teaming
Reframe a problem at broader or narrower levelsAbstraction Laddering
Resolve opposing positions through shared needsConflict Resolution Diagram
Generate combinations across independent dimensionsZwicky Box
Run an end-to-end creative problem-solving processProductive Thinking Model
Prevent failure by reasoning backwardInversion
Decompose a problem or solution spaceIssue Trees
Rebuild from fundamental constraints and truthsFirst Principles
Move from events to patterns, structures, and beliefsIceberg Model
Map causal relationships and feedback loopsConnection Circles
Map concepts and explicit propositionsConcept Map
Explain goal-seeking or stabilizing behaviorBalancing Feedback Loop
Explain compounding growth or declineReinforcing Feedback Loop
Give specific, behavior-based feedbackSituation-Behavior-Impact
Lead a message with its conclusionMinto Pyramid

Combine Models Deliberately

  • Use one model by default.
  • Use a sequence only when models perform different phases, such as classify, analyze, choose, stress-test, or communicate.
  • Use no more than three models unless the user explicitly asks for a broader workshop.
  • Do not combine near-duplicates merely to appear thorough.
  • Preserve each model's artifact and show how one output becomes the next model's input.
  • Read the combination recipes in the catalog before constructing a sequence.

Response Shape

Adapt the headings to the request, but include these elements when useful:

  1. Frame — outcome, scope, constraints, and known evidence.
  2. Selected model(s) — name and concise selection rationale.
  3. Inputs and assumptions — clearly labeled.
  4. Model artifact — matrix, tree, map, sequence, draft, or structured notes.
  5. Interpretation — insights, trade-offs, uncertainty, and sensitivity.
  6. Action — decision, next step, owner, experiment, or review trigger.

Model References

Read only the cards needed for the current request.

Decision making

Problem solving

Systems thinking

Communication

What ships with it: 43 files

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