MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
AI Team OS 里用 CC 内置 Workflow 的两件事：产出回写 OS 的标准模板（§1-2、§4），以及审查分级与派工档位纪律（§3/§3.1）。准备调用 Workflow 编排子 agent 时，或要判定一件事该按 L0/L1/L2 哪一档审查（含「这活要不要开 workflow」）时使用。
Install Os Workflow
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CronusL-1141/AI-company os-workflow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/os-workflow .claude/skills/os-workflow && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .claude/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CronusL-1141/AI-company os-workflow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugin/skills/os-workflow .agents/skills/os-workflow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .agents/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CronusL-1141/AI-company os-workflow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugin/skills/os-workflow .cursor/skills/os-workflow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .cursor/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CronusL-1141/AI-company.git --path plugin/skills/os-workflow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CronusL-1141/AI-company os-workflow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugin/skills/os-workflow .gemini/skills/os-workflow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .gemini/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CronusL-1141/AI-company os-workflowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugin/skills/os-workflow .github/skills/os-workflow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .github/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CronusL-1141/AI-company --skill os-workflow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CronusL-1141/AI-company os-workflow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CronusL-1141/AI-company.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugin/skills/os-workflow .opencode/skills/os-workflow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "os-workflow" agent skill from https://github.com/CronusL-1141/AI-company/tree/master/plugin/skills/os-workflow into .opencode/skills/os-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "os-workflow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
os-workflow- GitHub stars
- 371
- Token cost
- ~1.1k tokens
- SKILL.md length
- 248 words
- Files
- 1
- Skills in repo
- 5
- Repo updated
- First seen
- Licence
- MIT
At a glance
AI Team OS 里用 CC 内置 Workflow 的两件事：产出回写 OS 的标准模板（§1-2、§4），以及审查分级与派工档位纪律（§3/§3.1）。准备调用 Workflow 编排子 agent 时，或要判定一件事该按 L0/L1/L2 哪一档审查（含「这活要不要开 workflow」）时使用。
- Works in 4 steps: 总任务上墙（Leader 职责） → 在每个 workflow agent 的 prompt 里嵌入「回写指令」 → 模型档位纪律 → …
- Agent Workflows work in your project
- SKILL.md covers 背景, 1. 总任务上墙（Leader 职责）, 2. 在每个 workflow agent 的 prompt… and 3. 模型档位纪律, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Os Workflow is an agent skill from CronusL-1141/AI-company. AI Team OS 里用 CC 内置 Workflow 的两件事：产出回写 OS 的标准模板（§1-2、§4），以及审查分级与派工档位纪律（§3/§3.1）。准备调用 Workflow 编排子 agent 时，或要判定一件事该按 L0/L1/L2 哪一档审查（含「这活要不要开 workflow」）时使用。
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. It works with Model Context Protocol. The repository describes itself as: Multi-agent team operating system for Claude Code. 108 MCP tools, 40+ agent templates, 10 lifecycle hooks, 7 pipeline workflows. Persistent teams, structured meetings, task wall… The licence is MIT.
When your agent uses it
- Agent Workflows work in your project
Example prompts
- “/os-workflow”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 3275b8a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Os Workflow loads about 1.1k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 248 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from CronusL-1141/AI-company at commit 3275b8a, republished under its MIT licence (© CronusL-1141). 248 words, ~1,071 tokens.
Download SKILL.mdSave it as
.claude/skills/os-workflow/SKILL.md (or your agent's skills folder).- name
- os-workflow
- description
- AI Team OS 里用 CC 内置 Workflow 的两件事：产出回写 OS 的标准模板（§1-2、§4），以及审查分级与派工档位纪律（§3/§3.1）。准备调用 Workflow 编排子 agent 时，或要判定一件事该按 L0/L1/L2 哪一档审查（含「这活要不要开 workflow」）时使用。
OS Workflow — 用 CC 工作流，但让产出回流 OS
背景
调用 Workflow 后，每个内部 agent 会被 hook 自动注册成一个 OS 团队（workflow-<wf_id>，
一次 workflow = 一个团队）。追踪是自动的，但工作内容不会自己入库——下面两件事必须你做。
1. 总任务上墙（Leader 职责）
调用 Workflow 前用 task_create 把这次工作方向登记上墙并置 running（§2 的回写指令要把它的 id
插进每个 agent 的 prompt，事后补建来不及），完成后 task_update 置 completed 并填 result。
2. 在每个 workflow agent 的 prompt 里嵌入「回写指令」
把下面这段粘进你写的 workflow 脚本里每个 agent() 的 prompt 末尾（已验证 workflow
agent 能调 OS 的 MCP 工具 + HTTP API，非沙盒）：
【回写 OS（收尾必做）】
1. ToolSearch 加载：select:mcp__ai-team-os__task_memo_add,mcp__ai-team-os__report_save
2. 完成本职工作后：
- task_memo_add(task_id="<总任务id>", content="<这步干了啥+关键结论>", memo_type="progress")
- 重要产出再 report_save(...) 落库，并把 report_id 写进 memo
3. 你在项目目录运行，MCP 自动带项目头，无需关心端口/项目 id。在脚本里把 <总任务id> 用第 1 步 task_create 拿到的 id 通过 prompt 字符串插值传进去。
脚本写法示例
// Leader 先 task_create 拿到 taskId（OS MCP），再写 workflow：
const WRITEBACK = `\n【回写 OS（收尾必做）】\n1. ToolSearch: select:mcp__ai-team-os__task_memo_add\n2. 完成后 task_memo_add(task_id="${taskId}", content="...", memo_type="progress")\n3. 项目目录运行，MCP 自动带项目头。`
const r = await agent('你的实际任务……' + WRITEBACK, { schema, label })3. 模型档位纪律
不传 model 即继承主会话模型，所以每个 agent() 都显式写 model（层级别名如 'opus'、'fable'，浮动到最新，不写死型号）。选哪一档按使用者自己的派工策略（写在项目 CLAUDE.md 或用户规则里），本技能不预设档位。
- 每处
model: 'fable'调用须配一条// fable 理由: …行注释；Agent 工具派工则在 prompt 首行写[fable 理由: …]。S6 派工门禁（PreToolUse 机检）：缺省 model 直接拦，fable 无理由拦。
// 每个 stage 显式写 model；用 fable 的那处配一条理由注释
const found = await parallel(ITEMS.map(x => () =>
agent(findPrompt(x) + WRITEBACK, { model: 'opus', schema: FINDINGS })))
// fable 理由: <这一步为什么要用这一档>
const verdict = await agent(judgePrompt(found) + WRITEBACK,
{ model: 'fable', effort: 'xhigh', schema: VERDICT })注：effort 与档位同按使用者自己的派工策略设（agent() 的 effort?: 'low'|'medium'|'high'|'xhigh'|'max'，省略即继承会话档位），S6 不校验 effort。模型档位则是硬约束：S6 对缺省 model、fable 无理由一律 exit(2) 拦下（见上一条）。
3.1 用量七规则（2026-09-05 用户裁定）
实证（0905 单日）：133 个 agent 产出 274 万 token 却读了 2.02 亿缓存（74:1）——钱花在重复读同一批全文上，不是花在干活上。
总原则：限制的是「把任何问题都按最复杂方式做完」，不是把复杂问题做简单；审查强度按风险定，不按预算定——L2 该全文对抗就全文，省的是重复读、多余的镜头与反驳者、多余的轮次。分级在前，模式在后：ultracode 常开≠事事开 workflow（用户习惯常开；它的字面定义「每个实质任务都开 workflow」照做就是给每件事按最贵的价做），L0/L1 直接做，只有 L2 才开；开了也按下面七条控人数与读量。
- 机检先行。 哈希、数字一致性、措辞残留、编号连续、禁用字符、格式，凡能写成命令的检查先跑脚本，零 token 秒出。agent 只处理脚本查不了的：逻辑、措辞立不立得住、设计取舍。
- 审查分级。 派出的活按风险分三档，档位决定审不审、怎么审：
- L0 自测闭环：改一个函数、调配置、跑工具。执行者自测通过即交付，不触发审查。前提是自测覆盖了改动路径；没覆盖的自动升 L1（实锤：自测 61/61 全绿的构建在改动路径上必崩）。
- L1 摘要审查：加一个模块功能、修普通 bug。审查者只读需求、架构基线、执行者 ≤300 字回传，不读执行过程。
- L2 全文对抗：跨模块重构、核心接口、安全或性能路径、对外发布物、进签名或哈希台账的产物。全文读，但一个审稿人读一遍，不是多镜头加多反驳者。
- 反驳者配额。 每条发现 1 个反驳者；它说"是真的"即接受，说"是假的"才加第二个复核（假反驳比假确认贵）。反驳者的档位按派工策略定；机械类发现（数字、哈希）用执行档即可。
- 审查给定位。 prompt 写"看第 X 节，参照第 Y 行"，禁止默认"读全文加全部参照文档"。多镜头并行时先按镜头切分对象，重叠的发现在派出前合并。
- 停机规则。 一轮折入后先跑机检；只有上一轮还剩脚本查不了的判断级 blocker 才开下一轮 agent 审查，否则一个终审即止。
- 回传上限。 执行者只回变更清单、验证结果、决策与风险，≤300 字；工具输出、调试记录、中间上下文用完即弃，不回传。
- Leader 不在大上下文里逐条改大文档。 一份文档超过十处改动，Leader 写成改动清单（定位 + 旧文 + 新文 + 理由）派一个 opus 在小上下文里执行，或整节 Write 一次；Leader 只亲手改一两处。实锤：0905 Leader 70 次 Edit 各在 40 万 token 上下文里重读，1.27 亿缓存读比子 agent 总和还多一半。
// 分级示例：L2 全文对抗 = 一个审稿人 + 每条发现一个反驳者，被反驳才二审
const findings = await agent(reviewPrompt(SECTIONS), { model: 'opus', schema: FINDINGS }) // 定位到章节
const verified = await parallel(findings.map(f => () =>
agent(refutePrompt(f), { model: 'opus', schema: VERDICT })
.then(async v => v.refuted
? { ...f, votes: [v, await agent(refutePrompt(f), { model: 'opus', schema: VERDICT })] } // 被反驳才二审；此处档位按你的派工策略
: { ...f, votes: [v] })))4. 结构化输出体量纪律（两次生产实锤）
给 agent 配 schema 时，prompt 里必须写显式体量硬约束（每字段字符上限、条目数上限、"宁可精炼不可超限"）——只靠 schema 的 maxLength 拦不住：agent 超限会陷入 StructuredOutput 重试循环，耗尽重试上限（5）后整路阵亡返回 null。
- schema 的
maxLength给出安全余量；长文本产出改让 agent 直接 Write 文件，结构化输出只返摘要与路径。 - 某一路阵亡后用 resume 修复：只改失败路的 prompt，其余路
(prompt, opts)不动，走缓存零成本重放。
要点
- 回写走 MCP 工具优先（自动项目隔离）；HTTP 兜底的端口以
~/.claude/data/ai-team-os/api_port.txt为准（默认 8000，被占用时 autostart 会换端口）。
© CronusL-1141, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in plugin/skills/os-workflow of CronusL-1141/AI-company.
Open the folder on GitHubat commit 3275b8a
Compare with similar skills
Os Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Os Workflow this skillCronusL-1141/AI-company | 371 | — | ~1.1k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| MemPalace Memory SearchMemPalace/mempalace | 59k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Crush Configurationcharmbracelet/crush | 29k | — | ~3.7k | Automated safety check: Pass | Custom licence |
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Os Release
CronusL-1141/AI-company
发布 AI Team OS 新版本的完整清单——预检、版本七处锁步、中英双语 CHANGELOG、双份 dist 构建、私有术语扫描、commit/tag、双仓推送、建 GitHub Release 条目并核对 latest 徽章、事后核对。当准备发版、补建漏掉的 Release 条目、或核对已发版本的线上状态时使用。
Meeting Participate
CronusL-1141/AI-company
你被派来参加 AI Team OS 会议（派你的 prompt 里带 meetingid），且要在 Round 2 及之后发言时用：怎么拿到自己的 agentid、怎么引用前人发言、roundnumber 怎么填。Round 1 的材料、发言规则与调用样例已经写在派你的 prompt 里，不必读本技能。
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Questions about Os Workflow
What does Os Workflow do?
AI Team OS 里用 CC 内置 Workflow 的两件事：产出回写 OS 的标准模板（§1-2、§4），以及审查分级与派工档位纪律（§3/§3.1）。准备调用 Workflow 编排子 agent 时，或要判定一件事该按 L0/L1/L2 哪一档审查（含「这活要不要开 workflow」）时使用。. Os Workflow is an agent skill from CronusL-1141/AI-company.
When should I use Os Workflow?
Os Workflow fits situations like: agent Workflows work in your project.
How do I install Os Workflow in Claude Code?
Run `npx skills add CronusL-1141/AI-company --skill os-workflow -a claude-code`. Or copy the skill folder (plugin/skills/os-workflow in CronusL-1141/AI-company) into .claude/skills/os-workflow in your project. Claude Code loads it when a task matches its description.
How do I install Os Workflow in Codex?
Run `npx skills add CronusL-1141/AI-company --skill os-workflow -a codex`. Or copy the skill folder (plugin/skills/os-workflow in CronusL-1141/AI-company) into .agents/skills/os-workflow in your project. Codex loads it when a task matches its description.
Can I use Os Workflow in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CronusL-1141/AI-company --skill os-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/os-workflow, .gemini/skills/os-workflow, .github/skills/os-workflow and .opencode/skills/os-workflow in your project.
What does Os Workflow need to run?
SKILL.md names no scripts, command-line tools or credentials: Os Workflow is instructions for the agent only.
Does Os Workflow access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Os Workflow safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Os Workflow use?
Os Workflow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Os Workflow use?
About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Os Workflow?
Skills that share tags, products or a category with Os Workflow: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and MemPalace Memory Search (MemPalace/mempalace, 59k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Os Workflow?
CronusL-1141 (a GitHub user) maintains it in CronusL-1141/AI-company, which has 371 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 10, 2026.
Source: CronusL-1141/AI-company on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.