fpf:propose-hypotheses

Execute a First Principles Framework cycle to generate, verify, and audit hypotheses.

Updated Apr 6, 2026
One-click install
npx skills add https://github.com/luicabref97/sushi-jungle-web --skill fpf-propose-hypotheses
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fpf:propose-hypotheses
Source: https://github.com/luicabref97/sushi-jungle-web/tree/main/.agents/skills/fpf-propose-hypotheses
Command: npx skills add https://github.com/luicabref97/sushi-jungle-web --skill fpf-propose-hypotheses

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates a structured First Principles Framework (FPF) workflow to move from a raw problem statement to a documented, evidence-backed decision, reducing manual coordination and ensuring hypotheses are verified, validated, and audited before selection.

Core Features & Use Cases

  • End-to-end hypothesis lifecycle: scaffold a .fpf workspace, generate L0 hypotheses, verify logic to L1, validate evidence to L2, audit trust, and create a decision record.
  • Parallel agent orchestration: run verification, validation, and audit sub-agents in parallel to speed evaluation across many hypotheses.
  • Traceable artifacts: persist context, hypothesis files, audit reports, and design rationale records for review, reproducibility, and handoff.
  • Use Case: product teams investigating a sudden drop in retention can generate competing root-cause hypotheses, validate evidence streams, and produce a recommended action with rationale.

Quick Start

Propose hypotheses for why monthly active users dropped by 20% and produce an evidence-backed recommended decision.

Frequently Asked Questions about fpf:propose-hypotheses

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate and validate competing root-cause hypotheses for a product problem?▼

A first principles framework structures product investigations by moving from a raw problem statement to an evidence-backed recommended decision, generating L0 hypotheses, verifying logic to L1, validating evidence to L2, and auditing trust.

What's the best way to audit evidence backing for multiple decision hypotheses?▼

Auditing evidence for competing hypotheses is handled by parallel agent orchestration, running verification, validation, and audit sub-agents simultaneously to evaluate trust and produce traceable audit reports.

Do I need a repository workspace to run first principles hypothesis generation?▼

A repository workspace is required for first principles hypothesis generation to store .fpf artifacts, needing file read/write permissions to persist context, hypothesis files, audit reports, and decision rationale records.

Can I use parallel agent orchestration to speed up hypothesis verification and validation?▼

Parallel agent orchestration speeds up hypothesis evaluation by launching verification, validation, and audit sub-agents concurrently across multiple generated hypotheses.

How to create traceable decision records from raw problem statements?▼

Traceable decision records are created from raw problem statements by automating the end-to-end FPF lifecycle, persisting context, hypothesis files, and design rationale artifacts for review, reproducibility, and handoff.

When should I not use an automated first principles framework for product investigations?▼

An automated first principles framework should not be used for investigations lacking a clear problem statement or those without sufficient evidence streams to validate hypotheses and support a structured, auditable decision.