Creator · hyqibot
Last updated · Sep 4, 2026
Use this skill when another agent's expertise/context is needed, or when the user explicitly asks to involve another agent. First list agents, then use copaw agents chat for two-way communication with replies. | 当需要其他 agent 的专长/上下文,或用户明确要求调用其他 agent 时使用;先查 agent,再用 copaw agents c
Sandbox only
Install targets
Codex install prompt
Install the "multi_agent_collaboration" agent skill from https://github.com/hyqibot/token-free-openclaw/tree/main/copaw/agents/skills/multi_agent_collaboration. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use this skill when another agent's expertise/context is needed, or when the user explicitly asks to involve another agent. First list agents, then use copaw agents chat for two-way communication with replies. | 当需要其他 agent 的专长/上下文,或用户明确要求调用其他 agent 时使用;先查 agent,再用 copaw agents chat 双向通信(有回复) After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"hyqibot-multi-agent-collaboration","task":"Install multi_agent_collaboration","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaboration
Maintenance
fresh
16d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
121
67/100 Quality · 70/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
121 GitHub stars
Repo activity
121 stars, 33 forks
Maintenance
16d since push
License
Apache-2.0
Install
npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaboration
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaborationDo not use when
Alternative
16.3K Stars
npx skills add hardikpandya/stop-slop --skill stop-slop
Alternative
4.8K Stars
npx skills add cursor/plugins --skill unslop
Alternative
37.4K Stars
npx skills add blader/humanizer --skill humanizer
Alternative
5.8K Stars
npx skills add petergyang/no-ai-slop --skill no-ai-slop
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20multi_agent_collaboration%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20multi_agent_collaboration%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/hyqibot-multi-agent-collaboration/install
Agent should check
Copy prompt
Task: Use multi_agent_collaboration in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20multi_agent_collaboration%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/hyqibot-multi-agent-collaboration/install
Install command: npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaboration
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/hyqibot-multi-agent-collaboration/install
LLM text format
/api/skills/hyqibot-multi-agent-collaboration/install?format=text
Find alternatives
/api/skills/search?q=multi_agent_collaboration&limit=3
Agent prompt
Use multi_agent_collaboration for this task. Review https://www.openagentskill.com/api/skills/hyqibot-multi-agent-collaboration/install, then install with: npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaborationRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/hyqibot-multi-agent-collaboration
LLM text
/api/registry/manifest/hyqibot-multi-agent-collaboration?format=text
Install alias
/api/registry/install/hyqibot-multi-agent-collaboration
Recommend
/api/registry/recommend?task=Use%20multi_agent_collaboration%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Workflow automation
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO121 GitHub stars
Stars/forks activity
CHECK121 stars, 33 forks; issue activity unavailable in current metadata
Recent maintenance
PASS16d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Analyze markets
I need my agent to analyze markets, financial data, filings, portfolios, and quant strategies.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
--- name: multi_agent_collaboration description: Use this skill when another agent's expertise/context is needed, or when the user explicitly asks to involve another agent. First list agents, then use copaw agents chat for two-way communication with replies. | 当需要其他 agent 的专长/上下文,或用户明确要求调用其他 agent 时使用;先查 agent,再用 copaw agents chat 双向通信(有回复) metadata: builtin_skill_version: "1.2" copaw: emoji: "🤝" ---
# Multi-Agent Collaboration(多智能体协作)
## 什么时候用
当你**需要其他 agent 的专业能力、上下文、workspace 内容或协作支持**时,使用本 skill。 如果**用户明确要求某个 agent 参与/协助/回答**,也应使用本 skill。
### 应该使用 - 当前任务明显更适合某个专用 agent - 需要另一个 agent 的 workspace / 文件 / 上下文 - 需要第二意见或专业复核 - 用户明确要求某个 agent 参与或调用其他 agent
### 不应使用 - 你自己可以直接完成,且用户没有明确要求调用其他 agent - 只是普通问答,不需要专门 agent - 信息不足,应先追问用户 - 刚收到 Agent B 的消息,**不要再调用 Agent B**,避免循环
## 决策规则
1. **如果用户明确要求调用其他 agent,优先按要求执行** 2. **否则,能自己做,就不要调用** 3. **调用前先查 agent,不要猜 ID** 4. **需要上下文续聊时,必须传 `--session-id`** 5. **不要回调消息来源 agent**
---
## 最常用命令
### 1) 先查询可用 agents
```bash copaw agents list ```
### 2) 发起新对话(实时模式)
```bash copaw agents chat \ --from-agent <your_agent> \ --to-agent <target_agent> \ --text "[Agent <your_agent> requesting] ..." ```
### 3) 发起复杂任务(后台模式)
**复杂任务**包括:数据分析、报告生成、批量处理、外部API调用等。
```bash copaw agents chat --background \ --from-agent <your_agent> \ --to-agent <target_agent> \ --text "[Agent <your_agent> requesting] ..." ```
**输出**: ``` [TASK_ID: xxx-xxx-xxx] [SESSION: ...] ```
### 4) 查询后台任务状态
```bash copaw agents chat --background --task-id <task_id> ```
**重要**:不要频繁查询!提交任务后: 1. **不要硬等** - 继续处理其他任务或工作 2. **等待合理时间后再查** - 根据任务复杂度选择: - 简单分析:10-20 秒后查询 - 复杂分析:30-60 秒后查询 - 批量处理:1-3 分钟后查询 3. **在等待期间** - 可以回复用户、处理其他请求、或执行其他任务
### 5) 继续已有对话
```bash copaw agents chat \ --from-agent <your_agent> \ --to-agent <target_agent> \ --session-id "<session_id>" \ --text "[Agent <your_agent> requesting] ..." ```
**重点**: - 不传 `--session-id` = 新对话 - 传 `--session-id` = 续聊(保留上下文) - 复杂任务用 `--background`,提交后记录 task_id
---
## 任务模式选择
### 实时模式 vs 后台模式
| 任务类型 | 使用模式 | 命令 | |---------|---------|------| | 简单快速查询 | 实时模式 | `copaw agents chat` | | 复杂任务(数据分析、批量处理等) | 后台模式 | `copaw agents chat --background` |
**复杂任务示例**: - 分析大量数据或日志文件 - 生成详细报告 - 批量处理文件(10+ 个文件) - 调用慢速外部 API - 需要并行执行的独立任务
**判断标准**:如果不确定任务会花多长时间,或者任务很复杂,优先使用后台模式。
---
## 最小工作流
### 实时模式工作流
``` 1. 判断是否需要其他 agent,或用户是否明确要求调用 2. copaw agents list 3. copaw agents chat 发起对话 4. 从输出中记录 [SESSION: ...] 5. 后续需要上下文时带上 --session-id ```
### 后台模式工作流
``` 1. 判断任务是否复杂(数据分析、报告生成等) 2. copaw agents list 3. copaw agents chat --background 提交任务 4. 从输出中记录 [TASK_ID: ...] 5. 继续处理其他工作 6. 等待合理时间(30-60秒)后查询状态 7. 使用 --background --task-id 查询结果 ```
---
## 关键规则
### 必填参数
`copaw agents chat` 必须同时提供: - `--from-agent` - `--to-agent` - `--text`
### 身份前缀
消息建议以以下前缀开头:
```text [Agent my_agent requesting] ... ```
### 会话复用
首次调用会返回:
```text [SESSION: your_agent:to:target_agent:...] ```
后续续聊必须复制这个 session_id 传入 `--session-id`。
---
## 简短示例
### 用户明确要求调用其他 agent
```bash copaw agents list
copaw agents chat \ --from-agent scheduler_bot \ --to-agent finance_bot \ --text "[Agent scheduler_bot requesting] User explicitly asked to consult finance_bot. 请回答当前待处理的财务任务。" ```
### 新对话
```bash copaw agents chat \ --from-agent scheduler_bot \ --to-agent finance_bot \ --text "[Agent scheduler_bot requesting] 今天有哪些待处理的财务任务?" ```
### 续聊
```bash copaw agents chat \ --from-agent scheduler_bot \ --to-agent finance_bot \ --session-id "scheduler_bot:to:finance_bot:1710912345:a1b2c3d4" \ --text "[Agent scheduler_bot requesting] 展开第2项" ```
---
## 常见错误
### 错误 1:没先查 agent
不要猜 agent ID,先执行:
```bash copaw agents list ```
### 错误 2:想续聊但没传 session-id
这会创建新对话,丢失上下文。
### 错误 3:回调来源 agent
如果你刚收到 Agent B 的消息,不要再调用 Agent B。
---
## 可选命令
### 查看已有会话
```bash copaw chats list --agent-id <your_agent> ```
### 流式输出
```bash copaw agents chat \ --from-agent <your_agent> \ --to-agent <target_agent> \ --mode stream \ --text "[Agent <your_agent> requesting] ..." ```
### JSON 输出
```bash copaw agents chat \ --from-agent <your_agent> \ --to-agent <target_agent> \ --json-output \ --text "[Agent <your_agent> requesting] ..." ```
---
## 完整参数说明
### copaw agents list
**参数**: - `--base-url`(可选):覆盖API地址
**无必填参数**,直接运行即可。
### copaw agents chat
**必填参数**(实时模式): - `--from-agent`:发起方agent ID - `--to-agent`:目标agent ID - `--text`:消息内容
**后台任务参数**(新增): - `--background`:后台任务模式 - `--task-id`:查询任务状态(与 --background 一起使用)
**可选参数**: - `--session-id`:复用会话上下文(从之前的输出中复制) - `--new-session`:强制创建新会话(即使传了session-id) - `--mode`:stream(流式)或 final(完整,默认) - `--timeout`:超时时间(秒,默认300) - `--json-output`:输出完整JSON而非纯文本 - `--base-url`:覆盖API地址
---
## 后台任务模式详解(Background Task)
### 什么时候用后台模式?
当任务是**复杂任务**时,使用 `--background` 提交到后台:
✅ **应该使用后台模式**: - 数据分析(分析日志、统计数据) - 报告生成(生成长篇报告、文档) - 批量处理(处理多个文件) - 外部 API 调用(调用慢速服务) - 不确定任务时长的复杂任务
❌ **不需要后台模式**: - 简单快速查询 - 明确知道很快完成的任务
### 后台任务示例
#### 提交复杂任务
```bash copaw agents chat --background \ --from-agent scheduler \ --to-agent data_analyst \ --text "[Agent scheduler requesting] 分析 /data/logs/2026-03-26.log 中的用户行为,生成详细报告" ```
**输出**: ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [SESSION: scheduler:to:data_analyst:1774516703206:ec02e542]
✅ Task submitted successfully
Check status with: copaw agents chat --background --task-id 20802ea3-... ```
#### 查询任务状态
**重要**:提交后不要硬等!
1. **继续处理其他工作** - 回复用户其他问题、执行其他任务 2. **在合适时机查询** - 处理完其他工作后,或用户询问进度时 3. **如果必须等待** - 使用合理间隔(10-60秒),不要立即查询
```bash # 方式 1:处理其他任务后再查(推荐) # 提交任务后,继续完成用户的其他请求 # 在适当时机查询: copaw agents chat --background \ --task-id 20802ea3-832d-4fb4-86f0-666ad79fcc80
# 方式 2:如果必须等待,使用合理间隔 sleep 30 && copaw agents chat --background \ --task-id 20802ea3-832d-4fb4-86f0-666ad79fcc80 ```
**状态说明**:
任务状态分为两层: - **外层状态**(API 返回):`submitted` → `pending` → `running` → `finished` - **内层状态**(仅当外层是 `finished` 时):`completed`(成功)或 `failed`(失败)
**可能的输出**:
1. **已提交**(刚提交后立即查询可能看到): ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [STATUS: submitted]
📤 Task submitted, waiting to start...
💡 Don't wait - continue with other work! Check again in a few seconds: copaw agents chat --background --task-id 20802ea3-... ```
2. **等待执行**: ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [STATUS: pending]
⏸️ Task is pending in queue...
💡 Don't wait - handle other work first! Check again in a few seconds: copaw agents chat --background --task-id 20802ea3-... ```
4. **正在执行**: ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [STATUS: running]
⏳ Task is still running... Started at: 1774516703
💡 Don't wait - continue with other tasks first! Check again later (10-30s): copaw agents chat --background --task-id 20802ea3-... ```
5. **成功完成**: ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [STATUS: finished]
✅ Task completed
(任务结果内容...) ```
6. **执行失败**: ``` [TASK_ID: 20802ea3-832d-4fb4-86f0-666ad79fcc80] [STATUS: finished]
❌ Task failed
Error: (错误信息...) ```
### 查询间隔策略
**不要频繁查询!** 提交任务后应该:
1. **继续处理其他工作** - 不要硬等,去完成其他任务 2. **等待合理时间后再查** - 根据任务复杂度选择间隔 3. **避免阻塞当前流程** - 这是后台任务的核心价值
| 任务类型 | 建议首次查询 | 后续间隔 | 等待期间做什么 | |---------|------------|---------|--------------| | 简单分析 | 10 秒后 | 5-10 秒 | 处理其他用户请求 | | 复杂分析 | 30 秒后 | 10-20 秒 | 完成当前对话其他部分 | | 批量处理 | 1 分钟后 | 20-30 秒 | 执行其他独立任务 | | 超大任务 | 2 分钟后 | 30-60 秒 | 继续用户的其他工作 |
#### ✅ 推荐做法
**方式 1:处理其他任务后再查**(推荐) ```bash # 1. 提交任务,记录 task_id copaw agents chat --background ... # 返回 task_id
# 2. 继续处理用户的其他请求或任务 # (比如回答其他问题、执行其他操作)
# 3. 在适当时机查询结果 # (比如处理完当前任务后,或用户询问进度时) copaw agents chat --background --task-id <id> ```
**方式 2:定时轮询**(如果必须等待) ```bash # 递增间隔,先快后慢 sleep 10 && copaw agents chat --background --task-id <id> sleep 20 && copaw agents chat --background --task-id <id> sleep 30 && copaw agents chat --background --task-id <id> ```
#### ❌ 不要这样做
```bash # 错误:查询太频繁 while true; do copaw agents chat --background --task-id <id> sleep 1 # 太频繁了! done ```
---
## 帮助信息
随时使用 `-h` 查看详细帮助:
```bash copaw agents -h copaw agents list -h copaw agents chat -h ```
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for multi_agent_collaboration, ready for a manual X post.
multi_agent_collaboration: Use this skill when another agent's expertise/context is needed, or when the user explicitly... 121 stars https://www.openagentskill.com/skills/hyqibot-multi-agent-collaboration?ref=x
Listing + install path for multi_agent_collaboration: https://www.openagentskill.com/skills/hyqibot-multi-agent-collaboration?ref=x Install: npx skills add hyqibot/token-free-openclaw --skill multi_agent_collaboration
Listing source
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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stop-slop
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
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5.8K StarsPermission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness