Productivity

fpf:decay

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

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

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

skill.md

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Decision is obsolete, needs rethinking Deprecate Downgrade hypothesis, restart evaluation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:
    • Read valid_until from frontmatter
    • Compare with current date
    • Classify as FRESH, STALE, or EXPIRED

Step 2: Present Report

## 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:
# 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:
mv .fpf/knowledge/L2/auth-module.md .fpf/knowledge/L1/auth-module.md

Waive

User: "Waive the benchmark until February"

  1. Create waiver record:
# 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
general reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

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

  • Piyush G· Sep 9, 2024

    Keeps context tight: fpf:decay 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:decay matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sakshi Patil· Jul 7, 2024

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

  • Ganesh Mohane· Jun 6, 2024

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

  • Oshnikdeep· May 5, 2024

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

  • Dhruvi Jain· Apr 4, 2024

    fpf:decay 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:decay is focused, and the summary matches what you get after install.

  • Pratham Ware· Feb 2, 2024

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

  • Yash Thakker· Jan 1, 2024

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