fpf:query▌
neolabhq/context-engineering-kit · updated Apr 8, 2026
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Search the FPF knowledge base and display hypothesis details with assurance information.
Query Knowledge
Search the FPF knowledge base and display hypothesis details with assurance information.
Action (Run-Time)
- Search
.fpf/knowledge/and.fpf/decisions/by user query. - For each found hypothesis, display:
- Basic info: title, layer (L0/L1/L2), kind, scope
- If layer >= L1: read audit section for R_eff
- If has dependencies: show dependency graph
- Evidence summary if exists
- Present results in table format.
Search Locations
| Location | Contents |
|---|---|
.fpf/knowledge/L0/ |
Proposed hypotheses |
.fpf/knowledge/L1/ |
Verified hypotheses |
.fpf/knowledge/L2/ |
Validated hypotheses |
.fpf/knowledge/invalid/ |
Rejected hypotheses |
.fpf/decisions/ |
Design Rationale Records |
.fpf/evidence/ |
Evidence and audit files |
Output Format
## Search Results for "<query>"
### Hypotheses Found
| Hypothesis | Layer | Kind | R_eff |
|------------|-------|------|-------|
| redis-caching | L2 | system | 0.85 |
| cdn-edge | L2 | system | 0.72 |
### redis-caching (L2)
**Title**: Use Redis for Caching
**Kind**: system
**Scope**: High-load systems, Linux only
**R_eff**: 0.85
**Weakest Link**: internal test (0.85)
**Dependencies**:
[redis-caching R:0.85] └── (no dependencies)
**Evidence**:
- ev-benchmark-redis-caching-2025-01-15 (internal, PASS)
### cdn-edge (L2)
**Title**: Use CDN Edge Cache
**Kind**: system
**Scope**: Static content delivery
**R_eff**: 0.72
**Weakest Link**: external docs (CL1 penalty)
**Evidence**:
- ev-research-cdn-2025-01-10 (external, PASS)
Search Methods
By Keyword
Search file contents for matching text:
/fpf:query caching
-> Finds all hypotheses with "caching" in title or content
By Specific ID
Look up a specific hypothesis:
/fpf:query redis-caching
-> Shows full details for redis-caching
-> Displays dependency tree
-> Shows R_eff breakdown
By Layer
Filter by knowledge layer:
/fpf:query L2
-> Lists all L2 hypotheses with R_eff scores
By Decision
Search decision records:
/fpf:query DRR
-> Lists all Design Rationale Records
-> Shows what each DRR selected/rejected
R_eff Display
For L1+ hypotheses, read the audit section and display:
**R_eff Breakdown**:
- Self Score: 1.00
- Weakest Link: ev-research-redis (0.90)
- Dependency Penalty: none
- **Final R_eff**: 0.85
Dependency Tree Display
If hypothesis has depends_on, show the tree:
[api-gateway R:0.80]
└──(CL:3)── [auth-module R:0.85]
└──(CL:2)── [rate-limiter R:0.90]
Legend:
R:X.XX= R_eff scoreCL:N= Congruence Level (1-3)
Examples
Search by keyword:
User: /fpf:query caching
Results:
| Hypothesis | Layer | R_eff |
|------------|-------|-------|
| redis-caching | L2 | 0.85 |
| cdn-edge-cache | L2 | 0.72 |
| lru-cache | invalid | N/A |
Query specific hypothesis:
User: /fpf:query redis-caching
# redis-caching (L2)
Title: Use Redis for Caching
Kind: system
Scope: High-load systems
R_eff: 0.85
Evidence: 2 files
Query decisions:
User: /fpf:query DRR
# Design Rationale Records
| DRR | Date | Winner | Rejected |
|-----|------|--------|----------|
| DRR-2025-01-15-caching | 2025-01-15 | redis-caching | cdn-edge |
How to use fpf:query on Cursor
AI-first code editor with Composer
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:query
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches fpf:query from GitHub repository neolabhq/context-engineering-kit and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate fpf:query. Access the skill through slash commands (e.g., /fpf:query) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★33 reviews- ★★★★★Harper Diallo· Dec 12, 2024
We added fpf:query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Nia Abebe· Dec 12, 2024
Solid pick for teams standardizing on skills: fpf:query is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Dec 8, 2024
Registry listing for fpf:query matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aisha Jackson· Dec 8, 2024
fpf:query reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Meera Liu· Dec 8, 2024
I recommend fpf:query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· Nov 27, 2024
Keeps context tight: fpf:query is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Meera Sharma· Nov 27, 2024
Useful defaults in fpf:query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Rahul Santra· Nov 7, 2024
fpf:query reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Nia Farah· Nov 3, 2024
fpf:query has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Oct 26, 2024
We added fpf:query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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