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mastepanoskimastepanoski/ce-ai/skills/sequential-thinking/SKILL.md

sequential-thinking

Skill6 starsChanged 20 days agoMITView on GitHub

Dynamic, reflective step-by-step problem solving and hypothesis refinement

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Projects with it also use Problem-Solving Techniques, mobile-development and 3 more.

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  • AWSSkill

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The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 23 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Update Estimated [M] dynamically as problem complexity expands or narrows during exploration.

    line 23
  2. Distinctive:

    Do not rush to premature conclusions.

    line 24
  3. Very specific:

    When new evidence conflicts with an earlier premise, explicitly register a revision: Revision: Revising Thought [K] because [Empirical …

    line 32
  4. Distinctive:

    When multiple competing hypotheses remain viable, branch exploration explicitly and track parallel threads until sufficient evidence …

    line 34
  5. Distinctive:

    When executing sequential thinking, format reasoning steps using this structured block:

    line 48
  6. Distinctive:

    Verify absence of remaining counter-evidence.

    line 71

“Common” is measured: kept by at least 6 in 10 of the 23 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Sequential Thinking Protocol

Dynamic, reflective step-by-step problem solving and hypothesis refinement. Use this protocol when tackling complex non-linear problems, diagnosing elusive software defects, evaluating intricate architectural trade-offs, or navigating multi-variable decision spaces.

Core Operational Principles

  1. Explicit Step Progression & Dynamic Planning:
    • Maintain active thought progression in each reasoning step: Thought [N] / Estimated [M].
    • Update Estimated [M] dynamically as problem complexity expands or narrows during exploration.
    • Do not rush to premature conclusions; allow ideas to evolve and branch through systematic iterations.
  2. Hypothesis Formulation & Premise Tracking:
    • Explicitly articulate hypotheses before inspecting implementation details or executing tests.
    • Clearly delineate observed empirical facts from unverified assumptions and prior beliefs.
    • For every candidate hypothesis, specify what empirical evidence is required to prove or falsify it.
  3. Dynamic Revision & Branch Management:
    • When new evidence conflicts with an earlier premise, explicitly register a revision: Revision: Revising Thought [K] because [Empirical Reason].
    • When multiple competing hypotheses remain viable, branch exploration explicitly and track parallel threads until sufficient evidence eliminates alternatives.
  4. Falsification & Negative Evidence Testing:
    • Actively search for contradictory facts, boundary conditions, and counter-examples before declaring any hypothesis validated.
    • Rigorously test the contrapositive: "What would the system look like if this assumption were false?"
  5. Convergence & Actionable Synthesis:
    • Transition to convergence only after competing hypotheses have been systematically tested and either validated or falsified.
    • Summarize the final validated reasoning chain and state clear, actionable next steps.

Thought Execution Schema

When executing sequential thinking, format reasoning steps using this structured block:

### Thought [N] / Estimated [M]
- **Intent**: [What this specific thought intends to explore, verify, or calculate]
- **Premise / Hypothesis**: [Underlying claim or assumption being tested]
- **Empirical Evidence**: [Observed data, code snippets, test logs, or system constraints]
- **Falsification Check**: [Counter-evidence evaluated, edge cases probed, or disproving criteria]
- **Deduction & Revision**: [Validated findings; note if revising an earlier Thought [K]]
- **Trajectory**: [Continue linear path | Branch alternative | Revise previous | Converge to synthesis]

Exemplary Reasoning Flow

  1. Thought 1 / Estimated 4 (Problem Framing): Define the exact failure mode or architectural requirement; separate symptoms from candidate causes; set initial hypothesis space $H = {H_1, H_2}$.
  2. Thought 2 / Estimated 4 (Hypothesis Evaluation $H_1$): Inspect code and test logs against $H_1$; discover contradictory evidence in call flow; falsify $H_1$ with empirical citation.
  3. Thought 3 / Estimated 4 (Hypothesis Evaluation $H_2$ & Revision): Revision: Revising Thought 1 scope. Evaluate $H_2$ against observed state; find consistent supporting evidence across all test cases.
  4. Thought 4 / Estimated 4 (Convergence & Synthesis): Verify absence of remaining counter-evidence; synthesize root cause and formulate minimal, precise implementation plan.

Directives for Autonomous AI Agents

  • In-Context Execution: Execute the thought progression natively in your context window. Do not attempt to delegate to external RPC servers.
  • Evidence-First Grounding: Ground every deduction in concrete source references (file:// paths, line numbers, command outputs, or verified specifications).
  • Graceful Termination: Once convergence is achieved and verified, terminate sequential thoughts and proceed immediately to implementation or user reporting.

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