Borrowing it
Nothing to install: this file belongs to wangjialiang678/claude-omo-agentflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/wangjialiang678/claude-omo-agentflow/main/.claude/skills/agentflow/SKILL.mdgit clone --depth 1 https://github.com/wangjialiang678/claude-omo-agentflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wangjialiang678/claude-omo-agentflow/agentflow)<a href="https://agentmods.dev/skills/wangjialiang678/claude-omo-agentflow/agentflow"><img src="https://agentmods.dev/badge/skills/wangjialiang678/claude-omo-agentflow/agentflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wangjialiang678/claude-omo-agentflow/agentflow"><img src="https://agentmods.dev/badge/skills/wangjialiang678/claude-omo-agentflow/agentflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.01295 |
| Opus 5 | $0.00069 | $0.00647 |
| Sonnet 5 | $0.00028 | $0.00259 |
| Haiku 4.5 | $0.00014 | $0.00129 |
Grade A, and why
agentflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
掌天瓶 (Agentflow) — 异构多代理系统
代理委派策略
当需要执行以下类型的任务时，使用对应的子代理：
| 任务类型 | 委派给 | 模型路由 | 权限 |
|---|---|---|---|
| 后端代码实现 | backend-coder | Codex (via CCR) | Server-side only |
| 前端 UI/UX 实现 | frontend-coder | Gemini Pro (via CCR) | Client-side only |
| 代码审核/Review | reviewer | Codex (via CCR) | Read-only + test |
| 技术调研 | researcher | Gemini Flash (via CCR) | Read-only + web |
| 代码搜索/文件查找 | explorer | Gemini Flash (via CCR) | Read-only |
| 文档撰写 | doc-writer | Gemini Flash (via CCR) | .md only |
| 需求规划 | planner | Claude Opus (native) | Read-only |
| 核心代码 | 主代理自行处理 | Claude Opus (native) | Full |
完整注册表见 .claude/agentflow/agents.md。
三种执行模式
Pipeline（流水线）
多阶段链式执行，每阶段委派给不同代理。
触发方式：
- "按 review 流水线审查 {target}"
- "按 implement 流水线实现 {feature}"
- "按 research 流水线调研 {topic}"
- "按 debug 流水线修复 {bug}"
可用预设（定义在 .claude/agentflow/workflows/）：
| 预设 | 阶段 |
|---|---|
| review | explore → review → fix → verify |
| implement | plan → implement → review |
| research | explore → research → summarize |
| debug | explore → analyze → fix |
执行流程：
- 读取
.claude/agentflow/workflows/{预设}.yaml - 设置 workflow-state.json:
active=true, 记录 stages - 按顺序委派每个 stage 给对应代理
- 每个 stage 完成后更新 state
- Stop Hook 自动阻止中途停止
- 全部完成 →
active=false→ 允许停止
Autopilot（自主模式）
planner 规划 → 自动按计划执行。
触发方式：
- "@plan {需求描述}"
- "规划并实现 {feature}"
执行流程：
- 调用 planner 子代理生成
current-plan.md（含 TODO 列表） - 按 TODO 逐项委派给合适的代理
- 每完成一项更新 checkbox
- Stop Hook 检查未勾选 TODO → 阻止停止
- 全部完成 → 允许停止
Swarm（蜂群并行）
多 worker 从共享任务池并行处理。
触发方式：
- "并行修复所有 {问题类型}"
- "并行处理以下任务: {task1}, {task2}, ..."
执行流程：
- 分解为原子任务，写入 task-pool.json
- 启动多个子代理（background task）
- 每个子代理调用
claim-task.sh原子认领 - 完成后调用
complete-task.sh - Stop Hook 检查 task-pool → 阻止停止
- 全部完成 → 允许停止
任务池脚本（.claude/agentflow/scripts/）：
create-pool.sh <name> <tasks-file>— 创建任务池claim-task.sh <worker-id> [agent]— 原子认领complete-task.sh <task-id> [result]— 标记完成release-timeout.sh [seconds]— 释放超时任务pool-status.sh— 查看状态
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 140 lines · 139 tokens per session scan A 1c853f776251
agentflow is a skill published in the GitHub repository wangjialiang678/claude-omo-agentflow (2 stars, last pushed 6mo ago), licensed MIT. It adds 139 tokens to every session and 1,295 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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