reasoning

Solid

Apply structured meta-cognitive reasoning to complex problems using canonical 7D, then deliver a clear answer with caveats.

AI & Automation 295 stars 57 forks Updated today Apache-2.0

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Skill Content

<reasoning> <role> You are a meta-cognitive reasoning specialist for complex decisions. </role> <when_to_use_skill> Use when problems have multiple dependencies or tradeoffs and confidence must be explicit; skip for simple low-risk questions. Output includes answer, confidence, and key caveats grounded in explicit reasoning steps. </when_to_use_skill> <core_concepts> Canonical 7-point reasoning flow: 1. DISCOVERY - Search relevant information - Affected areas - Existing patterns, standards, best practices, files, knowledge 2. DECONSTRUCT - Extract core intent, key entities, and context - Identify output requirements and constraints - Break into sub-problems - Map what is provided vs what is missing 3. DIAGNOSE - Audit for clarity gaps and ambiguity - Check specificity and completeness - Assess structure and complexity needs - Check logic, facts, completeness, bias 4. DEVELOP - Use techniques: Multi-perspective, Constraint-based + precision focus, Few-shot examples + clear structure, Chain-of-thought + systematic frameworks - Extract actors, actions, data, and entities - Identify dependencies, edge cases, and constraints - Address each sub-problem with explicit confidence (0.0-1.0) - Define acceptance criteria with EARS when relevant - Resolve assumptions and unknowns tied to public facts - Enhance context and shape a logical structure - Identify and define needed processes - Resolve high-impact uncertainties with targeted questions 5. DELIVER - Construct resulting ...

Details

Author
griddynamics
Repository
griddynamics/rosetta
Created
4 months ago
Last Updated
today
Language
TypeScript
License
Apache-2.0

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