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Agent Skill · 开发

code-agent-orchestration(代码 Agent 编排)

code-agent-orchestration 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

code-agent-orchestration 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 3,123。

总安装 12,664周安装 544GitHub Stars 1下载 4,439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
MIT-0
版本
3.2.0

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:code-agent-orchestration(代码 Agent 编排)
来源仓库:https://github.com/goldmar/code-agent-orchestration
安装命令:
openclaw skills install code-agent-orchestration
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install code-agent-orchestration

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

从 OpenClaw 编排编码代理会话的技能。涵盖启动、监控、计划批准、生命周期管理和工作树决策。

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 OpenClaw;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 openclaw skills install code-agent-orchestration;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

    它需要执行命令,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

code-agent-orchestration 怎么安装?

在终端执行 openclaw skills install code-agent-orchestration。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

code-agent-orchestration 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

code-agent-orchestration 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install code-agent-orchestration 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

code-agent-orchestration 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

code-agent-orchestration 用的人多吗?

公开数据是累计安装 12,664 次,近一周安装 544 次,下载 4,439 次,GitHub 1 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。

code-agent-orchestration 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
code-agent-orchestration

代码 Agent 编排 · 本页

12,6641执行命令MIT-0有提醒

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

name
Code Agent Orchestration
description
Skill for orchestrating coding agent sessions from OpenClaw. Covers launching, monitoring, plan approval, lifecycle management, and worktree decisions.
metadata
openclaw
homepage
https://github.com/goldmar/openclaw-code-agent
requires
bins
install
package
openclaw-code-agent
bins
[]

Code Agent Orchestration

Use openclaw-code-agent to run Claude Code or Codex sessions as background coding jobs from chat.

Launch

  • Do not pass channel manually. Routing comes from agentChannels, the current chat context, and fallbackChannel.
  • Sessions are multi-turn. Continue existing work with agent_respond or agent_launch(..., resume_session_id=...); do not start a fresh session for the same task.
  • Always set a short kebab-case name when you care about later follow-up.
  • Set workdir to the target repo.
  • Use permission_mode: "plan" when the user wants a real review gate before implementation.
  • Use permission_mode: "bypassPermissions" only for autonomous execution.
  • defaultWorktreeStrategy now defaults to off. Opt into a worktree strategy explicitly when you want branch isolation.
  • In plan mode, the plan belongs in normal session output. Do not ask the coding agent to write plan docs or transcript artifacts unless the user explicitly asked for a file.

Example:

agent_launch(
  prompt: "Fix the auth middleware bug and add tests",
  name: "fix-auth",
  workdir: "/home/user/projects/my-app"
)

Resume, Don't Respawn

When a session already exists for the task, keep using it.

  • Waiting for plan approval: agent_respond(session, message, approve=true) or agent_request_plan_approval(...) if delegated approval must escalate to the user
  • Waiting for a question answer: agent_respond(session, message)
  • Killed/stopped by restart: agent_respond(session, message)
  • Completed but needs follow-up: agent_launch(resume_session_id=session_id, prompt="...")
  • Fresh agent_launch is only for genuinely independent work

Do not launch a new coding session from a wake event for the same task.

State and Monitoring

Use:

agent_sessions()
agent_output(session: "fix-auth", lines: 100)
agent_output(session: "fix-auth", full: true)

For worktree follow-through, inspect:

agent_worktree_status()
agent_worktree_status(session: "fix-auth")

Treat that tool's lifecycle, derived state, cleanup disposition, and retained reasons as authoritative. Do not infer cleanup safety from a transcript summary or from branch names alone.

Treat these wake fields as authoritative state when present:

  • requestedPermissionMode
  • effectivePermissionMode / currentPermissionMode
  • approvalExecutionState

Use those deterministic fields instead of inferring behavior from transcript fragments.

Approval/execution meanings:

  • approved_then_implemented: normal approved execution
  • implemented_without_required_approval: actual approval bypass
  • awaiting_approval: still stopped at the approval gate
  • not_plan_gated: no plan gate applied

Completion ownership:

  • The plugin sends the canonical completion notification.
  • The plugin owns the canonical completion status line; the orchestrator owns any additional plain-text follow-up.
  • After a coding-agent session completes, the orchestrator should usually add at least a short human-useful summary of what changed, what was done, or the concrete outcome.
  • That expectation applies to ordinary terminal/manual completions, manual no-change completions, and delegated worktree completions alike.
  • Treat the plugin's canonical ✅ as the status signal and your follow-up as the factual outcome summary that should usually come right after it.
  • That summary can be brief; one sentence is often enough.
  • Extra synthesis, risk framing, and next-step guidance are optional. Add them when useful; do not force them every time.
  • Do not generate your own heuristic completion summary from transcript tail lines. Base any summary on reliable result data such as agent_output(..., full=true), diff context, or deterministic tool state.
  • Skip the summary only in narrow cases:

- no user-facing follow-up will be sent at all because the orchestrator is silently continuing an internal multi-phase pipeline - the completion produced no meaningful outcome to report, or the reliable result data is still too incomplete to support even a short factual summary

Respond Rules

Auto-respond immediately only for:

  • permission requests for file reads, writes, or shell commands
  • explicit continuation prompts such as "Should I continue?"

Forward everything else to the user:

  • architecture or design choices
  • destructive operations
  • scope changes
  • credentials or production questions
  • ambiguous requirements

When forwarding, quote the session's exact question. Do not add commentary.

Plan Approval

Use permission_mode: "plan" whenever the user wants a real planning checkpoint.

planApproval: "ask"

  • Approval belongs to the user.
  • The plugin sends the canonical Approve / Revise / Reject prompt directly to the user.
  • If the user requests changes, wait for the revised plan from that same session; the revised submission becomes the latest actionable review version automatically.
  • Wait for the user's answer, then forward it with agent_respond(...).
  • Do not send a duplicate approval recap or second approval prompt.

planApproval: "delegate"

  • Approval belongs to the orchestrator first.
  • This is wake-first: the plugin wakes the orchestrator without user buttons.
  • Before deciding, read the full plan with agent_output(session, full=true); do not rely on the truncated preview.
  • Approve directly with agent_respond(..., approve=true) only when the latest actionable plan version is clearly in-bounds and low risk.
  • When approving directly, pass a structured rationale with approval_rationale, for example: agent_respond(session='...', message='Approved. Go ahead.', approve=true, approval_rationale='Scope matches the request and the changes are low risk.')
  • After approving directly, send the user a short plain-text follow-up explaining what was approved and why. The plugin's 👍 Plan approved line is only a fallback signal, not the full explanation.
  • If a prior version had changes_requested, that stale state should not block approval of the latest revised plan version.
  • If escalation is needed, call agent_request_plan_approval(session='...', summary='...') exactly once so the plugin sends the single canonical user approval prompt.
  • That escalation summary must concisely explain why you are escalating, plus risk/scope notes the user needs to decide.
  • After that canonical prompt exists, wait for the user's decision; do not send a second plain-text approval summary.

planApproval: "approve"

  • Auto-approve only after verification per the session policy.

Worktree Decisions

Treat worktrees as temporary task sandboxes, not as generic branch inventory.

Lifecycle meanings:

  • pending_decision: still waiting for merge / PR / dismiss follow-through
  • pr_open: PR exists; preserve the sandbox
  • merged: normal ancestry merge landed
  • released: content already landed on the base branch even though SHAs differ after rebase, squash, or cherry-pick
  • dismissed: sandbox intentionally discarded
  • no_change: no committed delta

If agent_worktree_status reports released, treat that sandbox as already landed. Do not narrate it as “still unmerged” just because the branch appears ahead.

off

  • No worktree. The session runs in the main checkout.

ask

  • The plugin owns the user-facing completion/decision message and button UI.
  • Do not call agent_merge or agent_pr unless the user explicitly asks after that.
  • A completed ask-session worktree may later resolve as released if its content already landed on base through another path. Confirm that with agent_worktree_status(...) before deciding what follow-up is still needed.

delegate

  • The plugin wakes the orchestrator with diff context and no automatic user buttons.
  • Read the diff context and decide whether a local merge is clearly safe.
  • agent_merge is acceptable for low-risk, clearly scoped changes that match the task.
  • Never call agent_pr() autonomously in delegate flows. Escalate PR decisions to the user.
  • If the wake already says the plugin sent the canonical completion notification, do not repeat that status line, but you should still usually add a short summary of the completed outcome.

manual

  • Wait for an explicit user request before calling agent_merge or agent_pr.

Cleanup

  • Use agent_worktree_cleanup(mode: "preview_safe") to review what Clean all safe would remove.
  • Use agent_worktree_cleanup(mode: "clean_safe") only when the user asked to clean up safe sandboxes.
  • Use agent_worktree_cleanup(mode: "preview_all") when you need both safe candidates and retained reasons.
  • Respect retained reasons from agent_worktree_status / agent_worktree_cleanup; they are the lifecycle model, not advisory prose.

Never

  • Never use raw git merge or raw PR commands in place of plugin tools.
  • Never invent your own workaround for a pending worktree decision; use agent_worktree_cleanup(session: "...", dismiss_session: true) to dismiss permanently.
  • Never use agent_worktree_cleanup to force-delete unresolved worktrees. The supported bulk action is "clean all safe": omit session and let the plugin remove only lifecycle-safe worktrees while preserving anything active, pending, dirty, or PR-open.
  • Never merge or PR an ask worktree behind the user's back.

File Artifact Policy

  • Do not ask the coding agent to write planning documents, investigation notes, or analysis artifacts as files unless the user explicitly requested a file.
  • Do not commit planning documents, investigation notes, or transcript-summary artifacts to the branch.
  • Commit only actual code, configuration, tests, and explicitly requested documentation.

Anti-Patterns

  • Do not pass multi_turn or multi_turn_disabled; all sessions are multi-turn.
  • Do not pass channel manually unless you are debugging routing.
  • Do not auto-answer design or scope questions.
  • Do not infer approval/completion ownership from old transcript snippets when deterministic fields are present.
  • Do not post duplicate completion or approval recaps when the plugin already sent the canonical message.

平台分布

OpenClaw

81.83%
按下载量换算3,632

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install code-agent-orchestration 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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