Execute the First Principles Framework (FPF) cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, and produce a decision.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfpf:propose-hypothesesExecute the skills CLI command in your project's root directory to begin installation:
Fetches fpf:propose-hypotheses from neolabhq/context-engineering-kit and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate fpf:propose-hypotheses. Access via /fpf:propose-hypotheses in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Execute the First Principles Framework (FPF) cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, and produce a decision.
Problem Statement: $ARGUMENTS
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.
Launch fpf-agent with sonnet[1m] model:
Read ${CLAUDE_PLUGIN_ROOT}/tasks/init-context.md and execute.
Problem Statement: $ARGUMENTS
**Write**: Context summary to `.fpf/context.md`**
Launch fpf-agent with sonnet[1m] model:
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 |
|----|-------|------|-------|
| ... | ... | ... | ... |
.fpf/knowledge/L0/Condition: User says yes to adding hypotheses.
Launch fpf-agent with sonnet[1m] model:
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.
Condition: User finished adding hypotheses.
For EACH L0 hypothesis file in .fpf/knowledge/L0/, launch parallel fpf-agent with sonnet[1m] model:
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/.
For EACH L1 hypothesis file in .fpf/knowledge/L1/, launch parallel fpf-agent with sonnet[1m] model:
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/.
For EACH L2 hypothesis file in .fpf/knowledge/L2/, launch parallel fpf-agent with sonnet[1m] model:
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/.
Launch fpf-agent with sonnet[1m] model:
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>
.fpf/decisions/..fpf/decisions//fpf:status to check FPF state/fpf:actualize if codebase changesWorkflow complete when:
.fpf/ directory structure exists.fpf/context.md.fpf/decisions/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 RecordMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for fpf:propose-hypotheses matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: fpf:propose-hypotheses is the kind of skill you can hand to a new teammate without a long onboarding doc.
fpf:propose-hypotheses has been reliable in day-to-day use. Documentation quality is above average for community skills.
fpf:propose-hypotheses is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: fpf:propose-hypotheses is focused, and the summary matches what you get after install.
fpf:propose-hypotheses has been reliable in day-to-day use. Documentation quality is above average for community skills.
fpf:propose-hypotheses fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend fpf:propose-hypotheses for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
fpf:propose-hypotheses reduced setup friction for our internal harness; good balance of opinion and flexibility.
fpf:propose-hypotheses is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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