Productivity

fpf:query

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

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

Search the FPF knowledge base and display hypothesis details with assurance information.

skill.md

Query Knowledge

Search the FPF knowledge base and display hypothesis details with assurance information.

Action (Run-Time)

  1. Search .fpf/knowledge/ and .fpf/decisions/ by user query.
  2. 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
  3. 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 score
  • CL: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 |
general reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

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

  • Piyush G· Sep 9, 2024

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

  • Chaitanya Patil· Aug 8, 2024

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

  • Sakshi Patil· Jul 7, 2024

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

  • Ganesh Mohane· Jun 6, 2024

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

  • Oshnikdeep· May 5, 2024

    Useful defaults in fpf:query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Apr 4, 2024

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

  • Rahul Santra· Mar 3, 2024

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

  • Pratham Ware· Feb 2, 2024

    We added fpf:query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Jan 1, 2024

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