fpf-problem-solving

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Use First Principles Framework (FPF) to solve problems, architect systems, evaluate alternatives, decompose complexity, classify problems, define quality, recover methods from work evidence, steer or recovery-test work, screen mandatory workflow steps, discover consequence-bearing systems, compare configuration changes, clarify learning/development claims, probe capability loss, decide under uncertainty, establish causality, reason about time, synthesize architecture, govern ontic admission, request specialist results, synthesize ontologies, publish views, revalidate sources, select representations, assess explanations, appraise advice. Triggers: FPF, bounded contexts, SoTA packs, assurance calculus, decision theory, operational parsimony, causal/temporal reasoning, architecture, modularity, transformation-flow networks, narrative rendering, structural adequacy, cultural evolution, quality gates, lexical discipline, Parts A-I. Not for simple task planning, general philosophy, or Agile unrelated to FPF.

AI & Automation 149 stars 13 forks Updated today MIT

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Skill Content

# First Principles Framework (FPF) An "Operating System for Thought" — a transdisciplinary architecture for reasoning, written in human- and machine-readable pseudo-code. FPF turns raw intelligence (human or machine) into organisationally usable reasoning: explicit bounded contexts, auditable artefacts, multi-view descriptions, and disciplined hand-offs between specialised actors. ## Use cases Use FPF whenever you need to think more rigorously than the situation's default. - Decompose a messy, cross-domain problem into parts that can be reasoned about independently - Make a high-stakes decision with incomplete evidence — and know what evidence is still missing - Get a mixed team to reason together without vocabulary collisions or hidden assumptions - Audit whether a conclusion is well-founded or just plausible - Transform or transfer an insight without losing its subject, precision, or stated limitations - Structure a proposal that must survive scrutiny from multiple expert perspectives - Generate alternatives systematically instead of anchoring on the first idea - Define what "better" means before comparing options - Classify what kind of problem you're facing before searching for solutions - Plan how an AI agent should select and sequence its tools under budget and trust constraints - Make a decision under uncertainty — identify options, weigh evidence, and commit with an auditable rationale - Establish whether X causes Y — or just correlates — and determine what interv...

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Author
CodeAlive-AI
Repository
CodeAlive-AI/ai-driven-development
Created
8 months ago
Last Updated
today
Language
Python
License
MIT

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