Fpf

Structured reasoning plugin that makes AI decision-making transparent and auditable through hypothesis generation, logical verification, and evidence-based validation.

Focused on:

• Transparent reasoning - All decisions documented with full audit trails
• Hypothesis-driven analysis - Generate competing alternatives before evaluating
• Evidence-based validation - Computed reliability scores, not estimates
• Human-in-the-loop - AI generates options; humans decide (Transformer Mandate)

Plugin Target

• Make AI reasoning auditable - full trail from hypothesis to decision
• Prevent premature conclusions - enforce systematic evaluation of alternatives
• Build project knowledge over time - decisions become reusable knowledge
• Enable informed decision-making - trust scores based on evidence quality

Overview

The FPF plugin implements structured reasoning using the First Principles Framework methodology developed by Anatoly Levenchuk a methodology for rigorous, auditable reasoning. The killer feature is turning the black box of AI reasoning into a transparent, evidence-backed audit trail.

The core cycle follows three modes of inference:

• Abduction — Generate competing hypotheses (don't anchor on the first idea).
• Deduction — Verify logic and constraints (does the idea make sense?).
• Induction — Gather evidence through tests or research (does the idea work in reality?).

Then, audit for bias, decide, and document the rationale in a durable record.

The framework addresses a fundamental challenge in AI-assisted development: making decision-making processes transparent and auditable. Rather than having AI jump to solutions, FPF enforces generating competing hypotheses, checking them logically, testing against evidence, then letting developers choose the path forward.

> Warning: This plugin loads the core FPF specification into context, which is large (~600k tokens). As a result it loaded into a subagent with Sonnet[1m] model. But such agent can consume your token limit quickly.

Implementation based on quint-code by m0n0x41d.

Quick Start

# Install the plugin
/plugin install fpf@NeoLabHQ/context-engineering-kit

# Start a decision process
/propose-hypotheses What caching strategy should we use?

# Commad will perform majority of orcestration and launch subagents to perform the work.
# Additionaly you will be asked to add your own hypotheses and review the results.

Workflow Diagram

┌─────────────────────────────────────────────────────────────────┐
│ 1. Initialize Context                                           │
│    /propose-hypotheses <problem>                            │
│    (create .fpf/ directory structure)                           │
└────────────────────────┬────────────────────────────────────────┘
                         │
                         │ problem context captured
                         ▼
┌─────────────────────────────────────────────────────────────────┐
│ 2. Abduction: Generate Hypotheses                               │ ◀── add your own ───┐
│    (create L0 hypothesis files)                                 │                     │
└────────────────────────┬────────────────────────────────────────┘                     │
                         │                                                              │
                         │ 3-5 competing hypotheses                                     │
                         ▼                                                              │
┌─────────────────────────────────────────────────────────────────┐                     │
│ 3. User Input                                                   │                     │
│    (present summary, allow additions)                           │─────────────────────┘
…

Commands Overview

/propose-hypotheses - Decision Cycle

Execute the complete FPF cycle from hypothesis generation through evidence validation to decision.

• Purpose - Make architectural decisions with full audit trail
• Output - .fpf/decisions/DRR- - .md with winner and rationale

/propose-hypotheses [problem or decision to make]

Arguments

Natural language description of the decision or problem. Examples: "What caching strategy should we use?" or "How should we deploy our application?"

How It Works - ADI Cycle

The workflow follows three inference modes:

1. Initialize Context - Creates .fpf/ directory structure and captures problem constraints

1. Abduction: Generate Hypotheses - FPF agent generates 3-5 generate plausible, diverse, and competing hypotheses in L0 folder. How it works:

• You pose a problem or question
• The AI (as Abductor persona) generates 3-5 candidate explanations or solutions
• Each hypothesis is stored in L0/ (unverified observations)