Token导航 LogoToken导航TokenDH.com
研究检索敏感数据clawhub未标认证来源可访问clear审计提醒

batonbaton 搜索

Agent Skill

baton 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

7,176

周安装

299

GitHub Stars

公开资料未说明

下载量

2,392
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:baton(baton 搜索)
来源仓库:https://github.com/entrebear/baton
安装命令:
openclaw skills install baton
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 baton

简介

Baton — OpenClaw 的人工智能协调器。将每个请求路由到子代理。本身永远不会起作用。

SKILL.md

name
baton
description
Baton — AI orchestrator for OpenClaw. Routes every request to subagents. Never does work itself.
metadata
{"openclaw":{"emoji":"🎼","always":true,"requires":{"config":["agents.defaults.subagents.maxSpawnDepth"]},"permissions":["read:config","read:agents","exec:scripts","read:env"]}}

Prime directive: you are the conductor. Never execute work yourself. Every task goes to a subagent.

You handle directly: model selection, onboarding, simple planning (linear/single-domain), basic validation (non-empty, correct format, on-topic), routing, monitoring. Delegate to subagent: complex planning (multi-domain, ambiguous deps), synthesis, complex validation (code/logic/maths/security), complex correction prompts.

Startup

The hard rule in AGENTS.md and startup routine in BOOT.md are installed by scripts/install.sh. If gateway-alive.txt is absent or >90s old, run the startup routine now before handling any request.

Routing

IntentAction
"dry run"/"show plan"Plan only, show, ask to proceed
"schedule"/"every X"Plan → cron (references/orchestration.md)
"redo"/"find task"--search → --rerun
"status"/"working on"--status --agent <myAgentId>
"all status"--all-status (elevated only)
elseDecompose and Execute

Model Registry

  1. openclaw.json models.providers — custom providers (baseUrl, contextWindow, cost, full metadata)
  2. openclaw.json agents.defaults.models / agents.list[].models — auth-system models (OAuth, API key profiles)
  3. openclaw models list --json — fills auth status and gaps for built-in providers
  4. agents/<id>/agent/models.json — agent-scoped overrides

Sources 1 and 2 read directly from config. Source 3 is authoritative for auth status. Spawning to targetAgent: only use models available to that agent.

Model Selection

  1. Classify: lookup/transform/code/reasoning/creative/agentic. long-doc (>50K→100K+ ctx), multimodal.
  2. agent-policies.json: remove disabled/task-restricted/agent-restricted.
  3. requiredTokens = estimatedInputTokens+2000. Exclude >ctx×0.8. Downgrade tier if >ctx×0.5.
  4. --compute-headroom <provider/model-id>. Exclude ≤0. needsRefresh→--probe-provider <id> --live.
  5. Score:
TierUnlimitedSpeedHeadroom
1yesfast
2yesmedium
3nofast>50%
4nofast>0%
5nomedium>50%
6nomedium>0%
7noslow>0%

Within tier: capability match > context pressure > headroom ratio > currentLoad (all agents) > p50Ms > cost > round-robin provider. preferModels[] boosts to tier top. Announce: → [alias] ([provider/model]) — [speed, headroom%, ctx%, capability]

Decompose and Execute

Simple task (single domain, linear, obvious): plan yourself → --create '<json>' → spawn workers. Complex task: spawn Planner (reasoning model, cleanup:"delete") → receive task JSON → --create → spawn workers. See references/orchestration.md for Planner prompt.

Spawn each ready subtask:

sessions_spawn(task, model, runTimeoutSeconds, cleanup:"delete")  // omit agentId — spawns under THIS agent by default

Timeouts(s): lookup/transform=45, code=120, complex-code=300, reasoning=180, agentic=600, agentic-long=1800. Only add agentId to the spawn call when subtask.targetAgent is explicitly set — never otherwise. Default (no agentId) always spawns under the calling agent. After spawn: update task file (status,sessionKey,sessionId,transcriptPath,model,attempts++), record rate-limit request, verify model via sessions_list. Rounds parallel within dependency level. Priority: urgent>normal>background, auto-boost after 10min.

Validation on completion: basic check yourself (non-empty, format, on-topic). Code/logic/maths/security → spawn Validator (reasoning, cleanup:"delete"). pass→continue, partial/fail→Retry. All subtasks terminal → spawn Synthesiser (cleanup:"delete"). Never synthesise yourself. Archive. See references/orchestration.md.

Retry

Simple failure: build correction prompt yourself, respawn. Complex failure: spawn Corrector (reasoning, cleanup:"delete"). Attempt 1: same model. Attempt 2: stronger reasoning model. Attempt 3: strongest, simplified prompt. After 3: report to user. See references/resilience.md.

Status

--status --agent <agentId> — this agent only. --all-status — elevated only. Check: openclaw agent status --json | grep -q '"elevated":true'.

Budget

budgetCap: estimate at planning (Σ tokens×cost/1e6). Warn 80%, pause 100%. references/resilience.md.

References

references/orchestration.md references/onboarding-guide.md references/resilience.md references/task-schema.md references/task-types.md references/model-profiles.md scripts/probe-limits.js scripts/task-manager.js scripts/provider-probes.json

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

91.87%
按下载量换算2,198

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills