
from ai-driven-development58
A structured "thinking OS" for rigorous problem decomposition, decision-making, and cross-disciplinary reasoning using bounded contexts, trust scoring, and mult
FPF (First Principles Framework) is an organised, auditable architecture for complex reasoning. It helps teams and agents decompose problems into bounded contexts, assign roles, evaluate confidence, generate systematic alternatives, and compose disciplined proposals that survive expert scrutiny. The framework provides patterns for provenance, trust/assurance scoring (F-G-R), reasoning cycles, and composition operators for combining partial solutions.
Use FPF when confronting high-stakes or cross-domain problems that need explicit boundaries, traceable assumptions, or multi-stakeholder convergence — e.g., architecture decisions, policy design, cross-discipline research, agent orchestration under budget/trust constraints, or auditing claims for reliability. Avoid for trivial task planning or generic brainstorming.
Best suited for agents exposed to long-form spec content (Claude Code, Copilot-style assistants, Codex-like tools) and for humans using it as a structured prompt/guide. It can be used by agent orchestrators that understand multi-step reasoning and tool gating.
FPF is a transdisciplinary reasoning framework that decomposes problems into bounded contexts with trust scoring. The SKILL.md is comprehensive with a detailed routing table across 20 sections, but extremely verbose. The bundled split_spec.py script is well-written but failed to run because it requires a git submodule (FPF-Spec.md) that isn't available in the audit sandbox. No security concerns — the script only does local file I/O.
FPF git submodule (FPF-Spec.md) not availablePure reasoning/documentation skill with no network calls or security risks. The script is a spec splitter that's well-structured but tightly coupled to a submodule. SKILL.md routing table is useful but the overall skill is very heavy for practical agent use due to sheer size.