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Decay

OrganizationPopular
NeoLabHQ
decay

Manage evidence freshness by identifying stale decisions and providing governance actions

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill namedecay
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Decay AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/decay .claude/skills/decay
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decay in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Decay on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Decay is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

SituationActionWhat it does
Evidence is old but decision is still goodRefreshRe-run the test, get fresh evidence
Decision is obsolete, needs rethinkingDeprecateDowngrade hypothesis, restart evaluation
Accept risk temporarilyWaiveRecord 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

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

ActionWhat's Recorded
Deprecatefrom_layer, to_layer, reason, date
Waiveevidence_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

Frequently asked questions

What does the Decay AI skill do?

Manage evidence freshness by identifying stale decisions and providing governance actions

Why use Decay on TypingMind?

Because you install it once and use it with any model. Decay is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Decay in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/decay. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decay?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Decay?

As many as you like. As long as a model supports skills, you can use Decay with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Decay AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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