Token导航 LogoToken导航

Agent Skill · 待分类

fpf:decay(fpf 衰减)

fpf:decay 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

fpf:decay 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,暂无第三方扫描结果,按安装量在待分类类的 11,209 个 Skill 里排第 724。

总安装 8,811周安装 353GitHub Stars 891下载 2,852核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fpf:decay(fpf 衰减)
来源仓库:https://github.com/neolabhq/context-engineering-kit
仓库路径:skills/fpf:decay
安装命令:
npx skills add https://github.com/neolabhq/context-engineering-kit --skill fpf:decay
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

fpf:decay 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态和协作事项进行整理。可通过 npx skills add 命令从指定仓库安装并使用。

安装前需确认权限范围、维护状态及是否触发联网或文件操作。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/fpf:decay

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/neolabhq/context-engineering-kit --skill fpf:d…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

fpf:decay 怎么安装?

在终端执行 npx skills add https://github.com/neolabhq/context-engineering-kit --skill fpf:decay。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

fpf:decay 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

fpf:decay 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

fpf:decay 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

fpf:decay 用的人多吗?

公开数据是累计安装 8,811 次,近一周安装 353 次,下载 2,852 次,GitHub 891 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

fpf:decay 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
fpf:decay

fpf 衰减 · 本页

8,811891需要联网未明确未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

## 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:

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

平台分布

Codex

36.03%
按下载量换算1,028

Claude

27.8%
按下载量换算793

Cursor

19.37%
按下载量换算552

Gemini CLI

9.04%
按下载量换算258

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills