fpf:propose-hypotheses

neolabhq/context-engineering-kit · updated Apr 8, 2026

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$npx skills add https://github.com/neolabhq/context-engineering-kit --skill fpf:propose-hypotheses
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summary

Execute the First Principles Framework (FPF) cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, and produce a decision.

skill.md

Propose Hypotheses Workflow

Execute the First Principles Framework (FPF) cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, and produce a decision.

User Input

Problem Statement: $ARGUMENTS

Workflow Execution

Step 1a: Create Directory Structure (Main Agent)

Create .fpf/ directory structure if it does not exist:

mkdir -p .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}
touch .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}/.gitkeep

Postcondition: .fpf/ directory scaffold exists.


Step 1b: Initialize Context (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Initialize FPF context"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/init-context.md and execute.
    
    Problem Statement: $ARGUMENTS
    
    **Write**: Context summary to `.fpf/context.md`**
    

Step 2: Generate Hypotheses (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Generate L0 hypotheses"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/generate-hypotheses.md and execute.
    
    Problem Statement: $ARGUMENTS
    Context: <summary from Step 1b>
    
    **Write**: List of hypothesis IDs and titles to `.fpf/knowledge/L0/`
    
    Reply with summary table in markdown format:
    
      | ID | Title | Kind | Scope |
      |----|-------|------|-------|
      | ... | ... | ... | ... |
    

Step 3: Present Summary (Main Agent)

  1. Read all L0 hypothesis files from .fpf/knowledge/L0/
  2. Present summary table from agent response.
  3. Ask user: "Would you like to add any hypotheses of your own? (yes/no)"

Step 4: Add User Hypothesis (FPF Agent, Conditional Loop)

Condition: User says yes to adding hypotheses.

Launch fpf-agent with sonnet[1m] model:

  • Description: "Add user hypothesis"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/add-user-hypothesis.md and execute.
    
    User Hypothesis Description: <get from user>
    
    **Write**: User hypothesis to `.fpf/knowledge/L0/`
    

Loop: Return to Step 3 after hypothesis is added.

Exit: When user says no or declines to add more.


Step 5: Verify Logic (Parallel Sub-Agents)

Condition: User finished adding hypotheses.

For EACH L0 hypothesis file in .fpf/knowledge/L0/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Verify hypothesis: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/verify-logic.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L0/<hypothesis-id>.md
    
    **Move**: After you complete verification, move the file to `.fpf/knowledge/L1/` or `.fpf/knowledge/invalid/`.
    

Wait for all agents, then check that files are moved to .fpf/knowledge/L1/ or .fpf/knowledge/invalid/.


Step 6: Validate Evidence (Parallel Sub-Agents)

For EACH L1 hypothesis file in .fpf/knowledge/L1/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Validate hypothesis: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/validate-evidence.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L1/<hypothesis-id>.md
    
    **Move**: After you complete validation, move the file to `.fpf/knowledge/L2/` or `.fpf/knowledge/invalid/`.
    

Wait for all agents, then check that files are moved to .fpf/knowledge/L2/ or .fpf/knowledge/invalid/.


Step 7: Audit Trust (Parallel Sub-Agents)

For EACH L2 hypothesis file in .fpf/knowledge/L2/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Audit trust: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/audit-trust.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L2/<hypothesis-id>.md
    
    **Write**: Audit report to `.fpf/evidence/audit-{hypothesis-id}-{YYYY-MM-DD}.md`
    
    **Reply**: with R_eff score and weakest link
    

Wait for all agents, then check that audit reports are created in .fpf/evidence/.


Step 8: Make Decision (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Create decision record"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/decide.md and execute.
    
    Problem Statement: $ARGUMENTS
    L2 Hypotheses Directory: .fpf/knowledge/L2/
    Audit Reports: .fpf/evidence/
    
    **Write**: Decision record to `.fpf/decisions/`
    
    **Reply**: with decision record summary in markdown format:
    
    | Hypothesis | R_eff | Weakest Link | Status |
    |------------|-------|--------------|--------|
    | ... | ... | ... | ... |
    
    **Recommended Decision**: <hypothesis title>
    
    **Rationale**: <brief explanation>
    

Wait for agent, then check that decision record is created in .fpf/decisions/.

Step 9: Present Final Summary (Main Agent)

  1. Read the DRR from .fpf/decisions/
  2. Present results from agent response.
  3. Present next steps:
    • Implement the selected hypothesis
    • Use /fpf:status to check FPF state
    • Use /fpf:actualize if codebase changes
  4. Ask user if he agree with the decision, if not launch fpf-agent at step 8 with instruction to modify the decision as user wants.

Completion

Workflow complete when:

  • .fpf/ directory structure exists
  • Context recorded in .fpf/context.md
  • Hypotheses generated, verified, validated, and audited
  • DRR created in .fpf/decisions/
  • Final summary presented to user

Artifacts Created:

  • .fpf/context.md - Problem context
  • .fpf/knowledge/L0/*.md - Initial hypotheses
  • .fpf/knowledge/L1/*.md - Verified hypotheses
  • .fpf/knowledge/L2/*.md - Validated hypotheses
  • .fpf/knowledge/invalid/*.md - Rejected hypotheses
  • .fpf/evidence/*.md - Evidence files
  • .fpf/decisions/*.md - Design Rationale Record
how to use fpf:propose-hypotheses

How to use fpf:propose-hypotheses on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add fpf:propose-hypotheses
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/neolabhq/context-engineering-kit --skill fpf:propose-hypotheses

The skills CLI fetches fpf:propose-hypotheses from GitHub repository neolabhq/context-engineering-kit and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/fpf:propose-hypotheses

Reload or restart Cursor to activate fpf:propose-hypotheses. Access the skill through slash commands (e.g., /fpf:propose-hypotheses) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.567 reviews
  • Luis Gupta· Dec 28, 2024

    Registry listing for fpf:propose-hypotheses matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Luis Iyer· Dec 24, 2024

    Keeps context tight: fpf:propose-hypotheses is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Shikha Mishra· Dec 16, 2024

    fpf:propose-hypotheses has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ganesh Mohane· Dec 12, 2024

    fpf:propose-hypotheses is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hana Diallo· Dec 4, 2024

    Solid pick for teams standardizing on skills: fpf:propose-hypotheses is focused, and the summary matches what you get after install.

  • Camila Khan· Nov 23, 2024

    fpf:propose-hypotheses has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hana Abebe· Nov 23, 2024

    fpf:propose-hypotheses fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Advait Verma· Nov 15, 2024

    I recommend fpf:propose-hypotheses for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sophia Nasser· Nov 15, 2024

    fpf:propose-hypotheses reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Emma Abbas· Nov 11, 2024

    fpf:propose-hypotheses is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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