fpf-methodology
CommunityAuditable hypothesis-driven AI reasoning.
Authorccf
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a structured, auditable reasoning framework (FPF) to guide AI-assisted coding decisions, ensuring transparent decision trails and reusable insights.
Core Features & Use Cases
- Cycle-based inference: Abduction, Deduction, and Induction with Design Rationale Records (DRRs) to capture rationale and evidence.
- Stateful knowledge management: Uses a .quint/ store for contextual context, hypotheses, verifications, and decisions.
- Use Case: Teams building complex AI-enabled systems can rely on FPF to document decisions and justify choices during architecture reviews.
Quick Start
Use the FPF workflow to begin with /q0-init, then execute /q1-hypothesize, /q2-verify, /q3-validate, and /q5-decide to generate a Design Rationale Record.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: fpf-methodology Download link: https://github.com/ccf/claude-code-ccf-marketplace/archive/main.zip#fpf-methodology Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
Agent Skills Search Helper
Install a tiny helper to your Agent, search and equip skill from 223,000+ vetted skills library on demand.