problem-solver

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Structured reasoning frameworks for complex problems. 19 analytical frameworks, 12 cognitive bias detectors, 10 decomposition methods, 10 mental models, Cynefin domain classification, ethical dimension check, and 6 communication patterns. McKinsey-grade problem solving for AI coding assistants.

AI & Automation 86 stars 26 forks Updated 3 weeks ago MIT

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Skill Content

# problem-solver ## Purpose Structured reasoning utility for problems that resist straightforward analysis. Receives a problem statement, detects cognitive biases, selects the appropriate analytical framework, applies it step-by-step with evidence, and returns ranked solutions with a communication structure. Stateless — no memory between calls. Inspired by McKinsey problem-solving methodology and cognitive science research on decision-making errors. ## Calls (outbound) - `council` (L3): Step 6.5 — decorrelated judgment on whether a high-stakes framework conclusion holds, narrow trigger only (documented L3→L3 coordination — see Step 6.5) ## Called By (inbound) - `debug` (L2): complex bugs that resist standard debugging - `brainstorm` (L2): structured frameworks for creative exploration - `plan` (L2): complex architecture decisions with many trade-offs - `ba` (L2): requirement analysis when scope is ambiguous ## Execution ### Input ``` problem: string — clear statement of the problem to analyze context: string — (optional) relevant background, constraints, symptoms observed goal: string — (optional) desired outcome or success criteria mode: string — (optional) "analyze" | "decide" | "decompose" | "communicate" ``` ### Step 1 — Receive and Classify Read the `problem` and `context` inputs. Restate the problem in one sentence to confirm understanding. Classify the problem type: | Type | Signal Words | Primary Approach | |------|-...

Details

Author
Rune-kit
Repository
Rune-kit/rune
Created
6 months ago
Last Updated
3 weeks ago
Language
JavaScript
License
MIT

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