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FPF Reasoning

Verified

Falsification-based problem-solving with hypothesis generation and verification

By NeoLab 55,700 stars v1.0 Updated 2026-03-15
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About This Skill

# Evidence Freshness Management

Manages evidence freshness by identifying stale decisions and providing governance actions. Implements FPF B.3.4 (Evidence Decay).

Key principle: Evidence is perishable. Decisions built on expired evidence carry hidden risk.

---

Quick Concepts

What is "stale" evidence?

Every piece of evidence has a `valid_until` date. A benchmark from 6 months ago may no longer reflect current system performance. A security audit from before a major dependency update doesn't account for new vulnerabilities.

When evidence expires, the decision it supports becomes questionable - not necessarily wrong, just unverified.

What is "waiving"?

Waiving = "I know this evidence is stale, I accept the risk temporarily."

  • Use it when:
  • You're about to launch and don't have time to re-run all tests
  • The evidence is only slightly expired and probably still valid
  • You have a scheduled date to refresh it properly

A waiver is NOT ignoring the problem - it's explicitly documenting that you know about the risk and accept it until a specific date.

The Three Actions

| Situation | Action | What it does | |-----------|--------|--------------| | Evidence is old but decision is still good | Refresh | Re-run the test, get fresh evidence | | Decision is obsolete, needs rethinking | Deprecate | Downgrade hypothesis, restart evaluation | | Accept risk temporarily | Waive | Record the risk acceptance with deadline |

---

Action (Run-Time)

Step 1: Generate Freshness Report

  1. List all evidence files in `.fpf/evidence/`
  2. For each evidence file:
  3. - Read `valid_until` from frontmatter
  4. - Compare with current date
  5. - Classify as FRESH, STALE, or EXPIRED

Step 2: Present Report

```markdown ## Evidence Freshness Report

EXPIRED (Requires Action)

| Evidence | Hypothesis | Expired | Days Overdue | |----------|------------|---------|--------------| | ev-benchmark-2024-06-15 | redis-caching | 2024-12-15 | 45 | | ev-security-2024-07-01 | auth-module | 2025-01-01 | 14 |

STALE (Warning)

| Evidence | Hypothesis | Expires | Days Left | |----------|------------|---------|-----------| | ev-loadtest-2024-10-01 | api-gateway | 2025-01-20 | 5 |

FRESH

| Evidence | Hypothesis | Expires | |----------|------------|---------| | ev-unittest-2025-01-10 | validation-lib | 2025-07-10 |

WAIVED

| Evidence | Waived Until | Rationale | |----------|--------------|-----------| | ev-perf-old | 2025-02-01 | Migration pending | ```

Step 3: Handle User Actions

Based on user response, perform one of:

#### Refresh

User: "Refresh the redis caching evidence"

  1. Navigate to the hypothesis in `.fpf/knowledge/L2/`
  2. Re-run validation to create fresh evidence

#### Deprecate

User: "Deprecate the auth module decision"

  1. Move hypothesis from L2 to L1 (or L1 to L0)
  2. Create deprecation record:

```markdown # In .fpf/evidence/deprecate-auth-module-2025-01-15.md --- id: deprecate-auth-module-2025-01-15 hypothesis_id: auth-module action: deprecate from_layer: L2 to_layer: L1 created: 2025-01-15T10:00:00Z ---

# Deprecation: auth-module

Reason: Evidence expired, technology landscape changed

Next Steps: Run `/fpf:propose-hypotheses` to explore alternatives ```

  1. Move the hypothesis file:
  2. ```bash
  3. mv .fpf/knowledge/L2/auth-module.md .fpf/knowledge/L1/auth-module.md
  4. ```

#### Waive

User: "Waive the benchmark until February"

  1. Create waiver record:

```markdown # In .fpf/evidence/waiver-benchmark-2025-01-15.md --- id: waiver-benchmark-2025-01-15 evidence_id: ev-benchmark-2024-06-15 waived_until: 2025-02-01 created: 2025-01-15T10:00:00Z ---

# Waiver: ev-benchmark-2024-06-15

Evidence: ev-benchmark-2024-06-15 Hypothesis: redis-caching Waived Until: 2025-02-01 Rationale: Migration pending, will re-run after completion

Accepted By: User Created: 2025-01-15

WARNING: This evidence returns to EXPIRED status after 2025-02-01. ```

---

Natural Language Usage

You don't need to memorize evidence IDs. Just describe what you want.

Example Workflow

``` User: /fpf:decay

Agent shows report with stale evidence

User: Waive the benchmark until February, we'll re-run it after the migration.

Agent: Creating waiver for ev-benchmark-2024-06-15 until 2025-02-01. Rationale: "Re-run after migration"

[Creates .fpf/evidence/waiver-benchmark-2025-01-15.md]

User: The vendor API is being discontinued. Deprecate that decision.

Agent: Deprecating hypothesis-vendor-api from L2 to L1. [Moves file, creates deprecation record]

Next step: Run /fpf:propose-hypotheses to explore alternatives. ```

---

WLNK Principle

A hypothesis is STALE if *any* of its evidence is expired (and not waived).

This is the Weakest Link (WLNK) principle: reliability = min(all evidence). One stale piece makes the whole decision questionable.

---

Audit Trail

All actions are logged:

| Action | What's Recorded | |--------|-----------------| | Deprecate | from_layer, to_layer, reason, date | | Waive | evidence_id, until_date, rationale, date |

  • Files created in `.fpf/evidence/`:
  • `deprecate-{hypothesis}-{date}.md`
  • `waiver-{evidence}-{date}.md`

---

Common Workflows

Weekly Maintenance ``` /fpf:decay # See what's stale # For each stale item: refresh, deprecate, or waive ```

Pre-Release ``` /fpf:decay # Check for stale decisions # Either refresh evidence or explicitly waive with documented rationale # Waiver rationales become part of release documentation ```

After Major Change ``` # Dependency update, API change, security advisory... /fpf:decay # See what's affected # Deprecate obsolete decisions # Start new hypothesis cycle for replacements ```

Use Cases

  • Apply the FPF (First Principles Framework) methodology to problem solving
  • Break down complex problems into fundamental components for analysis
  • Generate solutions by reasoning from first principles rather than analogy
  • Build structured thinking workflows for strategic decision making
  • Apply systematic problem decomposition to engineering and business challenges

Pros & Cons

Pros

  • +Compatible with multiple platforms including claude-code, codex, gemini, cursor
  • +Well-documented with detailed usage instructions and examples
  • +Purpose-built for documentation & writing tasks with focused functionality

Cons

  • -No built-in analytics or usage metrics dashboard
  • -Configuration may require familiarity with documentation & writing concepts

FAQ

What does FPF Reasoning do?
Falsification-based problem-solving with hypothesis generation and verification
What platforms support FPF Reasoning?
FPF Reasoning is available on Claude Code, OpenAI Codex CLI, Gemini CLI, Cursor.
What are the use cases for FPF Reasoning?
Apply the FPF (First Principles Framework) methodology to problem solving. Break down complex problems into fundamental components for analysis. Generate solutions by reasoning from first principles rather than analogy.

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