fpf:status

Display the current state of the FPF knowledge base

771 stars

Best use case

fpf:status is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Display the current state of the FPF knowledge base

Teams using fpf:status should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/status/SKILL.md --create-dirs "https://raw.githubusercontent.com/NeoLabHQ/context-engineering-kit/main/plugins/fpf/skills/status/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/status/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How fpf:status Compares

Feature / Agentfpf:statusStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Display the current state of the FPF knowledge base

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Status Check

Display the current state of the FPF knowledge base.

## Action (Run-Time)

1. **Check Directory Structure:** Verify `.fpf/` exists and contains required subdirectories.
2. **Count Hypotheses:** List files in each knowledge layer:
    - `.fpf/knowledge/L0/` (Proposed)
    - `.fpf/knowledge/L1/` (Verified)
    - `.fpf/knowledge/L2/` (Validated)
    - `.fpf/knowledge/invalid/` (Rejected)
3. **Check Evidence Freshness:** Scan `.fpf/evidence/` for expired evidence.
4. **Count Decisions:** List files in `.fpf/decisions/`.
5. **Report to user.**

## Status Report Format

```markdown
## 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/`:
1. Read the `valid_until` field from frontmatter
2. Compare with current date
3. Classify:
   - **Fresh**: `valid_until` > today + 30 days
   - **Stale**: `valid_until` > today but < today + 30 days
   - **Expired**: `valid_until` < today

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

tech-stack:add-typescript-best-practices

771
from NeoLabHQ/context-engineering-kit

Setup TypeScript best practices and code style rules in CLAUDE.md

tdd:write-tests

771
from NeoLabHQ/context-engineering-kit

Systematically add test coverage for all local code changes using specialized review and development agents. Add tests for uncommitted changes (including untracked files), or if everything is commited, then will cover latest commit.

tdd:test-driven-development

771
from NeoLabHQ/context-engineering-kit

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

tdd:fix-tests

771
from NeoLabHQ/context-engineering-kit

Systematically fix all failing tests after business logic changes or refactoring

sdd:plan

771
from NeoLabHQ/context-engineering-kit

Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task

sdd:implement

771
from NeoLabHQ/context-engineering-kit

Implement a task with automated LLM-as-Judge verification for critical steps

sdd:create-ideas

771
from NeoLabHQ/context-engineering-kit

Generate ideas in one shot using creative sampling

sdd:brainstorm

771
from NeoLabHQ/context-engineering-kit

Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, alternative exploration, and incremental validation. Don't use during clear 'mechanical' processes

sdd:add-task

771
from NeoLabHQ/context-engineering-kit

creates draft task file in .specs/tasks/draft/ with original user intent

sadd:tree-of-thoughts

771
from NeoLabHQ/context-engineering-kit

Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation

sadd:subagent-driven-development

771
from NeoLabHQ/context-engineering-kit

Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates

sadd:multi-agent-patterns

771
from NeoLabHQ/context-engineering-kit

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.