How to install status
npx skills add https://github.com/neolabhq/context-engineering-kit --skill statusFull instructions (SKILL.md)
Source of truth, from neolabhq/context-engineering-kit.
name: status description: "Display the current state of the FPF knowledge base"
Status Check
Display the current state of the FPF knowledge base.
Action (Run-Time)
- Check Directory Structure: Verify
.fpf/exists and contains required subdirectories. - Count Hypotheses: List files in each knowledge layer:
.fpf/knowledge/L0/(Proposed).fpf/knowledge/L1/(Verified).fpf/knowledge/L2/(Validated).fpf/knowledge/invalid/(Rejected)
- Check Evidence Freshness: Scan
.fpf/evidence/for expired evidence. - Count Decisions: List files in
.fpf/decisions/. - Report to user.
Status Report Format
## FPF Status
### Directory Structure
- [x] .fpf/ exists
- [x] knowledge/L0/ exists
- [x] knowledge/L1/ exists
- [x] knowledge/L2/ exists
- [x] evidence/ exists
- [x] decisions/ exists
### Current Phase
Based on hypothesis distribution: ABDUCTION | DEDUCTION | INDUCTION | DECISION | IDLE
### Hypothesis Counts
| Layer | Count | Status |
|-------|-------|--------|
| L0 (Proposed) | 3 | Awaiting verification |
| L1 (Verified) | 2 | Awaiting validation |
| L2 (Validated) | 1 | Ready for decision |
| Invalid | 1 | Rejected |
### Evidence Status
| Total | Fresh | Stale | Expired |
|-------|-------|-------|---------|
| 5 | 3 | 1 | 1 |
### Warnings
- 1 evidence file is EXPIRED: ev-benchmark-old-2024-06-15
- Consider running `/fpf:decay` to review stale evidence
### Recent Decisions
| DRR | Date | Winner |
|-----|------|--------|
| DRR-2025-01-15-use-redis | 2025-01-15 | redis-caching |
Phase Detection Logic
Determine current phase by examining the knowledge base state:
| Condition | Phase | Next Step |
|---|---|---|
No .fpf/ directory | NOT INITIALIZED | Run /fpf:propose-hypotheses |
| L0 > 0, L1 = 0, L2 = 0 | ABDUCTION | Continue with verification |
| L1 > 0, L2 = 0 | DEDUCTION | Continue with validation |
| L2 > 0, no recent DRR | INDUCTION | Continue with audit and decision |
| Recent DRR exists | DECISION COMPLETE | Review decision |
| All empty | IDLE | Run /fpf:propose-hypotheses |
Evidence Freshness Check
For each evidence file in .fpf/evidence/:
- Read the
valid_untilfield from frontmatter - Compare with current date
- Classify:
- Fresh:
valid_until> today + 30 days - Stale:
valid_until> today but < today + 30 days - Expired:
valid_until< today
- Fresh:
If any evidence is stale or expired, warn the user and suggest /fpf:decay.
Example Output
## FPF Status
### Current Phase: DEDUCTION
You have 3 hypotheses in L0 awaiting verification.
Next step: Continue the FPF workflow to process L0 hypotheses.
### Hypothesis Counts
| Layer | Count |
|-------|-------|
| L0 | 3 |
| L1 | 0 |
| L2 | 0 |
| Invalid | 0 |
### Evidence Status
No evidence files yet (hypotheses not validated).
### No Warnings
All systems nominal.
Related skills
More from neolabhq/context-engineering-kit and the wider catalog.
prompt-engineering
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
context-engineering
Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
thought-based-reasoning
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
reflect
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
update-docs
Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.
multi-agent-patterns
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.