01 / THE SIGNAL

我们发现了什么

审计并安全地弃用过时的通用 AI 智能体指令。标题:GitHub - ooocooc/open-skill-sunset:审计并安全地弃用过时的通用 AI 智能体指令。

  • 来源:GitHub(发现于 2026-08-27)
  • 证据等级:D · 发现产品或需求信号,暂未获得可核验的商业证据。
  • 商业模式:待核验
  • 主题:独立产品
  • 初筛评分:20.8/100 · 收录 1 次
#独立开发#待验证#产品发现
02 / SOURCE & EVIDENCE

证据,比故事更重要。

发现产品或需求信号,暂未获得可核验的商业证据。

规则清洗与初筛,未经人工商业核验。原文语境、实际客户和付费情况仍需自行验证。

引用与数字披露

来源类型(原作者自述/第三方测算/媒体转引)需采集端标注,本版尚未落字段。

短句引用
作者
未标注
抓取日期
来源类型
未标注
数字口径
币种
未标注
口径
未标注
披露主体
未标注
披露日期
未标注

中文辅助译文(全文)

Skill Sunset

English | 简体中文 您的 AGENTS.md 只会越来越长。哪些规则仍在为它们的上下文创造价值?

Skill Sunset 是针对 AGENTS.md、CLAUDE.md 以及通用 SKILL.md 指令的本地、只读退役审计。它将经过验证的损坏项与重复项,与未经证实的"此规则已过时"的假设区分开来。TEST 不是 RETIRE。

无需全局安装。不修改源文件。不调用 AI API。不消耗模型配额。无需提供商凭据。

第三方集成:HOL Guard v3.0.18 包含一个针对已安装的 skill-sunset 审计面的命令安全扩展。查看合并后的集成。

如果 Skill Sunset 为您节省了一次有风险的清理操作,请为该仓库加星。如果判定有误,请分享脱敏的反例。

该截图来自当前 CLI 基于 test/fixtures/sample-setup 生成的一份真实报告:扫描 5 个文件,11 项发现,分别展示重复退役、陈旧引用更新、渐进式披露以及行为假设。打开动画演示。 它做什么

Skill Sunset 将确定性检查与保守的审查假设相结合: · 查找损坏的本地引用、过时的绝对路径、格式错误的 Skill frontmatter、过大的始终加载文件,以及可能的明文凭据; · 检测同名 Skill 并在推荐可恢复退役之前验证完整捆绑包; · 将模型时代补偿规则标记为 TEST,而非断言它们已过时; · 生成双语 HTML、Markdown、JSON、Codex/Claude 交接提示、实验模板以及回滚清单; · 将领域知识、安全规则、授权门控以及项目不变量排除在自动退役之外。

任何发现都不授权删除操作。TEST 意味着"评估此假设",而非"更新的模型已使该规则不再必要"。 四个真实案例一览

2026-09-01,当前版本扫描了维护者正在积极使用的两个 Skill 目录。路径、Skill 名称以及指令文本已脱敏:

观察到的问题判定有助于预防
在 macOS 上扫描时,一个 Skill 包含五个仅限 Windows 的路径UPDATE暴露当前操作系统无法运行的指令;首先验证该 Skill 是否有意为仅限 Windows
一个始终加载的条目有 2,517 行,约 17,984 个 tokenDEMOTE将低频文档带入每个任务
四条规则要求每个任务使用某个工具TEST在没有对比证据的情况下删除未经测试的规则——或保留不必要的调用
一条规则命名了一个具体模型UPDATE提示人工审查;此项因其仍然适用且符合意图,故予以保留

每个案例都有公开的、经过脱敏的复现脚本:阅读完整演示或运行测试夹具。 支持的环境

范围已支持并经验证
操作系统GitHub Actions 中的 Ubuntu、macOS 与 Windows
Node.js20、22 与 24
预设目标Codex (~/.codex) 与 Claude Code (~/.claude)
指令文件AGENTS.md、CLAUDE.md 与 SKILL.md
Agent 交接Codex 与 Claude Code 提示产物
其他配置任何显式传递给 audit 的有界目录

其他 Linux 发行版预期可与 Node.js 20+ 配合使用,但不在当前 CI 矩阵范围内。 快速开始

扫描 Codex 配置:

扫描 Claude Code 配置:

扫描任意有界目录并保留 CI 友好的 JSON 输出:

CLI 默认为 --lang auto。使用 --lang en 或 --lang zh-CN 选择报告主语言。每个捆绑包仍然包含可移植的英文与简体中文 HTML 页面。 完整示例:输入 → 发现 → 验证 → 回滚

假设 AGENTS.md 包含:

且 docs/deploy.md 不存在。 · 输入与快照。保留可恢复的副本,然后审计该目录。

· 发现。报告可以生成:

· UPDATE / broken-reference:runbook 目标无法解析; · UPDATE / context7-assumption:当前工具可用性需要验证; · TEST / unconditional-tooling:"每个任务"需要具有代表性的新旧评估。 · 修改与验证。首先验证真实的 runbook 路径与当前工具列表。仅修改确认过的过时引用;将无条件工具化作为单独的候选。重新运行审计与项目测试:

对于 TEST 项,填写 .skill-sunset/experiment-template.json。验证不运行任何命令:

执行需要单独的 --run。通过的实验仅能证明其编码的验收标准。 · 回滚。如果引用、测试或任务行为出现回退,恢复已保存的文件并重新运行审计:

生成的 rollback-manifest.json 在已授权的执行代理记录实际更改与哈希之前,刻意保持为空。 报告捆绑包

报告内容将用户主目录以下的目标脱敏为 $HOME/...;其他绝对目标表示为 $ABSOLUTE/<name>。终端仍会打印真实的本地报告位置,以便所有者可以打开它。

activation-checklist.md 有助于验证运行时是否实际为两个预期释义加载各 Skill,并拒绝一个附近的负面用例。核心组件不读取对话历史,也不调用 AI。如果运行时未暴露可靠的加载事件,结果将保持 UNKNOWN;缺乏证据绝不支撑 RETIRE。 判定 · MERGE:精确重复或同名 Skill 之间的冲突。 · UPDATE:过时路径、引用、工具或与版本耦合的指令。 · DEMOTE:有用的始终加载细节,应属于渐进式披露。 · RETIRE:在同一扫描根目录下,名称相同的字节级一致的完整通用 Skill 捆绑包;仅推荐可恢复的归档。 · TEST:在清理之前需要进行行为层面新旧评估的过时假设。 行为实验安全

实验清单默认为仅验证,除非显式传入 --run。命令在没有 shell 的情况下运行,默认接收一个最小的、非密钥的环境白名单。完整的环境继承——包括可能的提供商凭据——需要额外的 --inherit-env 标志与可信命令。

试运行与结果文件保留可执行名称、参数数量、命令哈希、输出大小与输出哈希,而非命令参数或输出正文。切勿在实验清单中放置凭据。 本地开发

GitHub Actions 运行 Gitleaks 以及完整的操作系统与 Node.js 矩阵。Gitleaks 是发布前的把关工具,并非证明可识别每种可能的凭据格式。

参见 CHANGELOG.md、CONTRIBUTING.md 与 SECURITY.md。 维护 · 源代码维护者:@ooocooc · npm 发布者:ooocoo · Bug 与功能请求:GitHub Issues · 贡献方式:CONTRIBUTING.md · 安全报告:SECURITY.md · 发布历史:CHANGELOG.md

该项目以尽力而为为基础维护,不保证响应 SLA。 当前边界

版本 0.3.0 实现了静态检查、保守的重复退役、本地化报告、路径脱敏、受控的命令实验框架、CI 严重性退出码、对抗性输出测试,以及本地 Skill 激活清单。自动化的会话使用适配器与任务质量适配器仍是未来的证据层。

译文由上游机器翻译生成,可能有误;判断请以英文原文为准。

英文原文(来源本站未改写)

Skill Sunset

English | 简体中文 Your AGENTS.md only grows. Which rules are still earning their context?

Skill Sunset is a local, read-only retirement audit for AGENTS.md, CLAUDE.md, and generic SKILL.md instructions. It separates verified breakage and duplicates from unproven “this rule is obsolete” hypotheses. TEST is not RETIRE.

No global installation is required. No source edits. No AI API. No model quota. No provider credentials.

Third-party integration: HOL Guard v3.0.18 includes a command-safety extension for the installed skill-sunset audit surface. View the merged integration.

If Skill Sunset saves you one risky cleanup pass, star the repository. If a verdict is wrong, share a redacted counterexample.

The screenshot comes from a real report generated by the current CLI from test/fixtures/sample-setup: 5 files scanned, 11 findings, with duplicate retirement, stale-reference updates, progressive disclosure, and behavior hypotheses shown separately. Open the animated walkthrough. What it does

Skill Sunset combines deterministic checks with conservative review hypotheses: · finds broken local references, stale absolute paths, malformed Skill frontmatter, oversized always-loaded files, and possible plaintext credentials; · detects same-name Skills and verifies the complete bundle before recommending recoverable retirement; · marks model-era compensation rules as TEST instead of claiming they are obsolete; · produces bilingual HTML, Markdown, JSON, Codex/Claude handoff prompts, an experiment template, and a rollback manifest; · excludes domain knowledge, safety rules, authorization gates, and project invariants from automatic retirement.

No finding authorizes deletion. TEST means “evaluate this hypothesis,” not “a newer model made this rule unnecessary.” Four real cases at a glance

On 2026-09-01, the current version scanned two Skill directories actively used by the maintainer. Paths, Skill names, and instruction text were redacted:

Observed problemVerdictWhat it helps prevent
One Skill contained five Windows-only paths when scanned on macOSUPDATESurface instructions the current OS cannot run; first verify whether the Skill is intentionally Windows-only
One always-loaded entry had 2,517 lines and about 17,984 tokensDEMOTECarrying low-frequency documentation into every task
Four rules required a tool for every taskTESTDeleting an untested rule—or retaining unnecessary calls—without comparison evidence
One rule named a concrete modelUPDATEPrompt manual review; this item was kept because it was current and intentional

Each case has a public, sanitized reproducer: read the full walkthrough or run the fixtures. Supported environments

AreaSupported and verified
Operating systemsUbuntu, macOS, and Windows in GitHub Actions
Node.js20, 22, and 24
Preset targetsCodex (~/.codex) and Claude Code (~/.claude)
Instruction filesAGENTS.md, CLAUDE.md, and SKILL.md
Agent handoffCodex and Claude Code prompt artifacts
Other setupsAny bounded directory passed explicitly to audit

Other Linux distributions are expected to work with Node.js 20+, but are not part of the current CI matrix. Quick start

Scan Codex configuration:

Scan Claude Code configuration:

Scan any bounded directory and keep CI-friendly JSON output:

The CLI defaults to --lang auto. Use --lang en or --lang zh-CN to select the primary report language. Every bundle still contains portable English and Simplified Chinese HTML pages. Complete example: input → finding → validation → rollback

Suppose AGENTS.md contains:

and docs/deploy.md does not exist. · Input and snapshot. Keep a recoverable copy, then audit the directory.

· Findings. The report can produce:

· UPDATE / broken-reference: the runbook target cannot be resolved; · UPDATE / context7-assumption: current tool availability needs verification; · TEST / unconditional-tooling: “for every task” needs representative old-versus-new evaluation. · Change and validate. Verify the real runbook path and current tool list first. Change only the confirmed stale reference; make unconditional tooling a separate candidate. Re-run the audit and the project tests:

For a TEST item, fill .skill-sunset/experiment-template.json. Validation runs no commands:

Execution requires a separate --run. A passing experiment proves only its encoded acceptance criteria. · Rollback. If references, tests, or task behavior regress, restore the saved file and re-run the audit:

The generated rollback-manifest.json is intentionally empty until an authorized execution agent records actual changes and hashes. Report bundle

Report contents redact targets below the user home as $HOME/...; other absolute targets are represented as $ABSOLUTE/<name>. The terminal still prints the real local report location so the owner can open it.

activation-checklist.md helps verify whether a runtime actually loads each Skill for two expected paraphrases and rejects one nearby negative case.The core does not read conversation history or call AI.If the runtime exposes no reliable load event, the result stays UNKNOWN;absence of evidence never supports RETIRE.Verdicts · MERGE: exact duplicates or conflicting same-name Skills. · UPDATE: stale paths, references, tools, or version-coupled instructions. · DEMOTE: useful always-loaded detail that belongs in progressive disclosure. · RETIRE: byte-identical complete generic Skill bundles with the same name inside one scan root;

only recoverable archival is recommended. · TEST: an obsolescence hypothesis requiring old-versus-new behavioral evaluation before cleanup.Behavioral experiment safety

Experiment manifests are validation-only unless --run is explicit. Commands run without a shell and receive a minimal non-secret environment allowlist by default. Full environment inheritance—including possible provider credentials—requires the additional --inherit-env flag and trusted commands.

Dry runs and result files retain executable names, argument counts, command hashes, output sizes, and output hashes instead of command arguments or output bodies. Never put credentials in an experiment manifest. Local development

GitHub Actions runs Gitleaks plus the full operating-system and Node.js matrix. Gitleaks is a publication guard, not proof that every possible credential format can be recognized.

See CHANGELOG.md, CONTRIBUTING.md, and SECURITY.md. Maintenance · Source maintainer: @ooocooc · npm publisher: ooocoo · Bugs and feature requests: GitHub Issues · Contributions: CONTRIBUTING.md · Security reports: SECURITY.md · Release history: CHANGELOG.md

The project is maintained on a best-effort basis without a guaranteed response SLA. Current boundary

Version 0.3.0 implements static checks, conservative duplicate retirement, localized reports, path redaction, a gated command experiment harness, CI severity exits, adversarial output tests, and a local Skill-activation checklist. Automated session-usage adapters and task-quality adapters remain future evidence layers.

出处https://github.com/ooocooc/open-skill-sunset抓取日期 · 采集源 GitHub

03 / EVIDENCE GAPS

这条还缺什么证据?

下面每条都由本条已有字段推出(等级、理由、商业模式、来源次数、是否演示), 本站不生成推测性结论;通用验证方法放在方法论页。

  • 可核验的收入或付费证据查官网定价页与付费口径;第三方数据源(如 GetLatka)只作旁证,需标注来源与时点。
  • 商业模式未定确认按席位/按用量/授权还是开源托管版收费;开源项目另查 LICENSE 与是否存在付费版。
  • 只有单一来源找一手站点或其他渠道是否重复出现同一产品;社区热帖数量不等于商业进展。

通用验证清单(谁有这个问题/谁愿意付费/一个人能交付哪一小步)见我们的筛选方法。

04 / SIGNAL HISTORY

发现时间线