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Agent Skill · 研究检索

dag-fleet(达格舰队)

dag-fleet 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

dag-fleet 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,000。

总安装 396周安装 17GitHub Stars 公开资料未说明下载 139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
unknown

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:dag-fleet(达格舰队)
来源仓库:https://github.com/quickcall-dev/skills
仓库路径:skills/dag-fleet
安装命令:
npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

dag-fleet 用于在 tmux 中启动多个带预算控制的并行执行 worker。支持 Claude 和 Codex 双 provider 配置及 DAG 依赖管理。强调操作者全权负责进程监控和异常处理,无自动恢复机制。适用于长时间运行或资源密集型任务的分布式执行场景。使用前需熟悉 tmux 操作和进程管理能力。

来源仓库内路径:skills/dag-fleet

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet;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

dag-fleet 怎么安装?

在终端执行 npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

dag-fleet 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

dag-fleet 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 npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet 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。

dag-fleet 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

dag-fleet 用的人多吗?

公开数据是累计安装 396 次,近一周安装 17 次,下载 139 次。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。

dag-fleet 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。

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。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、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。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
dag-fleet

达格舰队 · 本页

396—执行命令未明确有提醒

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

Fleet

A skill for running parallel claude -p or codex exec workers in tmux with budgets and DAG dependencies. Supports both Claude and Codex providers — set per-fleet or per-worker. Operator owns all kill / steer / re-direction — there is no auto-restart, no auto-verify, no babysitter loop.

When to use this skill (and when NOT to)

FIRST: prefer Claude Code's built-in Agent tool when any of these are true. It's simpler, faster, and avoids the fleet machinery entirely.

  • The work fits inside the current conversation
  • All sub-agents will finish in under 10 minutes
  • You'll synthesize the results in the same session
  • You don't need budget caps or dependency ordering

Reach for THIS skill only when ≥1 of these is true:

  • Persistence: the run will outlive the parent claude process (e.g. multi-hour fleet, user closes laptop)
  • Per-worker budgets: you need max_budget_usd: N enforced per worker
  • DAG dependencies: worker D must wait for A, B, C to finish first
  • Mixed models per worker: Sonnet researchers + Haiku validators in the same fleet
  • Tmux pane visibility: the user wants to attach to individual workers and watch them stream

If none of those apply, stop reading this skill and use the Agent tool.

What this skill is NOT

  • Not an auto-recovery system. If a worker fails or hangs, the operator decides what to do.
  • Not a babysitter. There is no orchestrate.sh, no stuck-detection that kills, no mid-flight steering.
  • Not for "spawn 3 quick lookups in parallel" — that's the Agent tool's job.

Available scripts

ScriptWhen to callArgs
launch.shStart a new fleet from a fleet.json you generated<fleet-root>
status.shShow what's running, what's done, live cost, last message per worker<fleet-name-or-root> [-v] [--watch] [--json]
kill.shStop one worker or the entire fleet (operator's hard stop)`<fleet-name-or-root> <worker-id>\all [--force]`
relaunch-worker.shAfter editing one worker's prompt.md, re-run just that worker<fleet-name-or-root> <worker-id>
report.shGenerate a markdown summary when the fleet is done<fleet-root>
view.shCapture a single worker's tmux pane content<fleet-name-or-root> <worker-id>
feed.shStream a unified event feed across all workers<fleet-name-or-root> [--agent <id>]

Utilities (in lib/):

UtilityPurposeUsage
dag-viz.pyVisualize fleet DAG structure (ASCII or mermaid)python3 ${CLAUDE_SKILL_DIR}/lib/dag-viz.py <fleet.json> [--mermaid]

All scripts accept either an absolute fleet-root path or a fleet name (resolved via ~/.claude/fleet-registry.json, populated automatically by launch.sh).

Launch procedure (MUST follow exactly)

When the user asks you to launch a fleet:

  1. Set FLEET_ROOT to the user's specified directory. Default to cwd if unspecified. Use absolute paths only.
  2. mkdir -p $FLEET_ROOT/workers
  3. Generate $FLEET_ROOT/fleet.json — see references/fleet-json-schema.md for the full schema. Required top-level fields: fleet_name, config, workers[]. Each worker needs id, type, task, model, max_turns, max_budget_usd. Use depends_on: [...] for DAG ordering.
  4. For each worker, create $FLEET_ROOT/workers/{id}/prompt.md. The prompt MUST include this line verbatim: Save ALL output files to $FLEET_ROOT/workers/{id}/output/ — use absolute paths. (Substitute the real fleet root and worker id.)
  5. Run: bash ${CLAUDE_SKILL_DIR}/scripts/launch.sh $FLEET_ROOT
  6. Do NOT write your own tmux/claude commands. launch.sh handles topo sort, tmux session creation, per-worker spawning, budgets, and the registry.
  7. ALWAYS tell the user the exact status command so they can monitor manually: bash ${CLAUDE_SKILL_DIR}/scripts/status.sh <fleet-name-or-root> This is mandatory after every launch. The user must be able to check status without asking you.

Re-running ONE worker (the addendum workflow)

The user has a finished fleet and wants to add 1-2 sources / change one worker's instructions:

  1. Edit $FLEET_ROOT/workers/{id}/prompt.md (add the new sources / instructions)
  2. Run bash ${CLAUDE_SKILL_DIR}/scripts/relaunch-worker.sh <fleet-name> {id}
  3. The worker's old session.jsonl is rotated to .bak, a fresh tmux window spawns, other workers are untouched
  4. The fleet's tmux session must still exist. If it's been killed, the user must launch.sh --force-relaunch the whole fleet — relaunch-worker.sh only works against a live fleet session.

If the user wants to re-run multiple workers, do it one at a time. There is no batch re-run; that's intentional.

Killing

There are two operator-initiated kill paths and no automatic kills:

  • kill.sh <fleet> <worker-id> — kill one worker. Sweeps subprocess descendants. Use this when you've decided a single worker is going down the wrong path.
  • kill.sh <fleet> all --force — tear down the entire fleet, kill all tmux windows, sweep every orphan subprocess, mark workers KILLED, unregister from the registry.

There is no steer.sh. There is no mid-flight redirection. The intentional workflow for "I want this worker to take a different direction" is: kill.sh it, edit prompt.md, relaunch-worker.sh. Three steps, fully under operator control.

Resetting a partially-run fleet

Use when the fleet ran partially (bad prompts, wrong models, hit a bug) and you want to re-launch from scratch without state collision.

bash ${CLAUDE_SKILL_DIR}/scripts/reset.sh <FLEET_ROOT> [--soft|--hard] [--dry-run] [--force]

Preserved (both levels): fleet.json structure, workers/{id}/prompt.md. Gone on --soft (default): prior run outputs archived to archive/<ts>/, tmux session killed, status fields in fleet.json cleared. Gone on --hard: everything under the fleet root except fleet.json + prompts; registry entry removed.

Refuses with exit 2 if live workers detected — pass --force to kill them first. Preview with --dry-run.

Worker types

The type field on each worker controls the --disallowed-tools set passed to claude. Pick one:

  • read-only — disallows: Bash, Edit, Write, Agent, WebFetch, WebSearch. Cannot write files. Only use for pure analysis where output is captured from assistant messages in session.jsonl.
  • write — disallows: Bash, Agent, WebFetch, WebSearch. Use for synthesizers and any worker that writes output files.
  • code-run — disallows: Agent, WebFetch, WebSearch (the typical default for build/test workers)
  • research — disallows: Bash, Edit, Agent (web access enabled). Use for researchers, not read-only.
  • reviewer — disallows: Bash, Edit, Agent, WebFetch, WebSearch. Has Read + Write only. Use for reviewers that write verdict files.
  • orchestrator — disallows: Agent, WebFetch, WebSearch, Edit

WARNING: read-only cannot write files. If a worker needs to save output (findings.md, synthesis.md, etc.), use write, research, reviewer, or code-run. Setting a synthesizer to read-only will burn its entire budget trying to find a Write tool.

See references/worker-types.md for the full permission matrix.

Provider support (Claude + Codex)

Workers can run on either claude (default) or codex (OpenAI Codex CLI). Set at fleet level or per-worker:

{
  "config": {
    "provider": "codex",
    "model": "gpt-5.4",
    "reasoning_effort": "medium"
  },
  "workers": [
    { "id": "researcher", "type": "research", "provider": "codex", "model": "gpt-5.4", "reasoning_effort": "medium" },
    { "id": "writer", "type": "write", "provider": "claude", "model": "sonnet" }
  ]
}

Codex-specific fields

FieldValuesDefaultScope
provider"claude" \"codex""claude"config + per-worker
reasoning_effort"low" \"medium" \"high"(none)config + per-worker, codex only

Codex model aliases

ModelUse case
gpt-5.4Flagship — strongest reasoning, recommended default
gpt-5.4-miniFast/cheap — validators, simple tasks
gpt-5.3-codexCoding-focused (migrating to gpt-5.4)

Codex limitations vs Claude

  • No --max-budget-usd — codex has no per-worker budget cap. Fleet-level cost tracking still works (estimated from token counts).
  • No --fallback-model — codex has no automatic model fallback.
  • No per-tool disabling — codex uses sandbox modes (read-only, workspace-write) instead of --disallowed-tools. Worker types are mapped automatically.
  • Web search — research workers get -c 'web_search="live"' automatically.
  • All output workers need workspace-write — codex read-only sandbox blocks ALL file writes including output.

DAG dependencies

{
  "id": "synthesizer",
  "depends_on": ["researcher-01", "researcher-02"]
}

launch.sh uses the shared lib/dag.sh primitives (Kahn's BFS-layered topo-sort) to order workers and waits for dependencies to emit a terminal result event before starting dependents. Cycles are detected before any tmux state is created — fleet exits 2 with CYCLE:a,b,... on stderr. Workers within a layer run in parallel up to max_concurrent. Use dag-viz.py to preview the DAG structure before launch.

Budgets

  • worker.max_budget_usd: N — per-worker hard cap, passed to claude --max-budget-usd
  • config.max_budget_fleet: N — total fleet cap; launch.sh stops launching new workers once this is exceeded (already-running workers are not killed, the cap is "no new spending")

STRICT RULES

  1. ALWAYS use the scripts above for EVERY operation. Never write your own tmux / claude commands.
  2. NEVER use the --bare flag with claude — causes auth failures.
  3. Fleet root = user's directory. Default to cwd. ALL fleet files go inside $FLEET_ROOT.
  4. Worker output paths must be absolute: $FLEET_ROOT/workers/{id}/output/. Tell the worker this in its prompt.md.
  5. launch.sh is the only way to start workers. relaunch-worker.sh is the only way to selectively re-run one. There is no other path.
  6. Operator owns kill and direction changes. Do not auto-kill, do not auto-restart, do not auto-redirect. If a worker is misbehaving, surface it to the user and let them decide.
  7. Do NOT invent missing scripts. If you find yourself wanting steer.sh, verify.sh, add-worker.sh, or orchestrate.sh — they were intentionally removed. Use the operator-owned workflow above instead.

Rationalizations to reject

Agent saysRebuttal
"The task is small enough that I can write the tmux commands myself"The skill exists to prevent the 15 things you'll forget (unset CLAUDECODE, --disallowed-tools, session naming, registry, topo sort). Use launch.sh.
"I'll use relaunch-worker.sh to restart all stuck workers at once"One at a time, intentional. Batch restart is how experiment 001 burned $20 — cache rebuilds on every worker compounded.
"The worker seems stuck — I should kill and restart it"Long thinking blocks look like hangs. Check status.sh or view.sh first. Only the operator kills workers.
"I should add a verify step after each worker finishes"There is no verify step. The operator reads output and decides. Auto-verify was removed after it caused more harm than the failures it caught.
"I'll just add --bare to speed things up"--bare causes auth failures. Never use it. This is STRICT RULE #2.

When to give up on this skill

If the user asks for behavior that requires auto-recovery, mid-flight steering, or per-worker validation loops, tell them this skill no longer does those things by design. Suggest:

  • For auto-recovery → they should run a watcher script themselves and call kill.sh + relaunch-worker.sh from it
  • For mid-flight steering → kill + edit prompt.md + relaunch-worker
  • For validation → they read the output files themselves and decide

The skill's surface area was deliberately reduced after experiments where automated behavior caused more harm than the failures it was trying to recover from.

$ARGUMENTS

平台分布

Codex

35.78%
按下载量换算50

Claude

30.52%
按下载量换算42

Cursor

18.07%
按下载量换算25

Gemini CLI

10.15%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/quickcall-dev/skills --skill dag-fleet 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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