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pi-messenger-crewpi 信使船员

Agent Skill

pi-messenger-crew 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。

总安装

288

周安装

8

GitHub Stars

11

下载量

65
CodexClaudeCursorGemini CLI

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

unknown

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:pi-messenger-crew(pi 信使船员)
来源仓库:https://github.com/dwsy/agent
仓库路径:skills/pi-messenger-crew
安装命令:
npx skills add https://github.com/dwsy/agent --skill pi-messenger-crew
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/dwsy/agent --skill pi-messenger-crew

简介

pi-messenger-crew 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。

  • 适用于消息分发协调、通信链路优化或多节点协作等分布式系统场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和操作边界。
  • 建议结合原始 README 核验具体用法,注意是否会触发联网或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Pi-Messenger Crew Skill

Use pi-messenger for multi-agent coordination and Crew task orchestration.

Quick Reference

Join the Mesh (Required First)

pi_messenger({ action: "join" })

Check Status

pi_messenger({ action: "status" })
pi_messenger({ action: "list" })  // See other agents
pi_messenger({ action: "feed" })  // Activity feed

Crew Workflow

1. Check Crew Agents

pi_messenger({ action: "crew.agents" })  // Verify 5 agents
pi_messenger({ action: "crew.install" }) // Informational: shows discovered sources

2. Plan from PRD

// Auto-discover PRD.md in current directory
pi_messenger({ action: "plan" })

// Or specify path
pi_messenger({ action: "plan", prd: "path/to/PRD.md" })

// Or pass an inline prompt (no PRD file needed)
pi_messenger({ action: "plan", prompt: "Scan the codebase for bugs focusing on error handling" })

// Re-plan with a steering prompt (wipes existing tasks, preserves progress notes)
pi_messenger({ action: "plan", prompt: "Split the auth module into login and registration" })

// Steer first-time planning (prompt injected into progress notes before planner runs)
pi_messenger({ action: "plan", prd: "docs/PRD.md", prompt: "focus on backend first" })

// Plan + auto-start autonomous work when planning completes
pi_messenger({ action: "plan" })  // auto-starts workers (default)

// Cancel active or stale planning
pi_messenger({ action: "plan.cancel" })

Re-planning rejects if any tasks are in_progress — stop or complete them first. The steering prompt is injected into planning-progress.md's Notes section where the planner reads it on every pass.

3. Work on Tasks

// Single wave (runs ready tasks once)
pi_messenger({ action: "work" })

// Autonomous (keeps running until done/blocked)
pi_messenger({ action: "work", autonomous: true })

// Override concurrency or model for this wave
pi_messenger({ action: "work", autonomous: true, concurrency: 4 })
pi_messenger({ action: "work", model: "claude-sonnet-4-20250514" })

4. Task Management

pi_messenger({ action: "task.list" })
pi_messenger({ action: "task.ready" })  // Tasks with no pending deps
pi_messenger({ action: "task.show", id: "task-1" })
pi_messenger({ action: "task.start", id: "task-1" })
pi_messenger({ action: "task.progress", id: "task-1", message: "Implemented auth middleware" })
pi_messenger({ action: "task.done", id: "task-1", summary: "What was done" })
pi_messenger({ action: "task.block", id: "task-1", reason: "Why blocked" })
pi_messenger({ action: "task.unblock", id: "task-1" })
pi_messenger({ action: "task.reset", id: "task-1" })
pi_messenger({ action: "task.reset", id: "task-1", cascade: true })  // Reset dependents too

// Create tasks manually
pi_messenger({ action: "task.create", title: "Implement auth", content: "Detailed spec...", dependsOn: ["task-1"] })

// Split a task into subtasks (two-phase: inspect then execute)
pi_messenger({ action: "task.split", id: "task-3" })  // Inspect: shows spec, deps, dependents
pi_messenger({ action: "task.split", id: "task-3", subtasks: [
  { title: "Subtask A", content: "..." },
  { title: "Subtask B", content: "..." }
] })  // Execute: creates subtasks, parent becomes milestone

5. Task Revision

// Revise a single task's spec (planner rewrites based on your prompt)
pi_messenger({ action: "task.revise", id: "task-3", prompt: "add error handling for network failures" })

// Revise a task and all its transitive dependents (subtree revision)
// Planner sees the full subtree and can add/remove/modify tasks
pi_messenger({ action: "task.revise-tree", id: "task-3", prompt: "split this into separate API and CLI tasks" })

Single revision rewrites one task's spec. Tree revision rewrites an entire subtree — the planner can modify existing tasks, add new ones (capped at 2x subtree size), remove pending ones, and rewire dependencies. Done tasks in the subtree are preserved; revisable tasks reset to todo.

Both revisions are mutually exclusive (only one revision at a time).

6. Review

// Review a task implementation
pi_messenger({ action: "review", target: "task-1" })

// Review the overall plan
pi_messenger({ action: "review", target: "plan", type: "plan" })

Overlay Keybindings

The Crew overlay (accessible via /messenger then tab to Crew) supports these keybindings:

Global: [+/-] Adjust worker concurrency (1-10), [c] Cancel active planning, [Esc] Close/back

Task list view: [↑/↓] Navigate, [Enter] Detail view

Task actions (list and detail):

  • [s] Start task (todo only)
  • [r] Reset task, [R] Cascade reset (reset + all dependents)
  • [u] Unblock task (blocked only)
  • [b] Block with reason (in_progress only)
  • [q] Stop worker (in_progress with live worker)
  • [m] Message worker (in_progress with live worker)
  • [S] Split task (shows hint with command)
  • [p] Revise task spec, [P] Revise subtree (not in_progress, not milestone)
  • [x] Delete task (not while worker is active)
  • [←/→] Navigate between tasks in detail view

File Coordination

// Reserve files before editing
pi_messenger({ action: "reserve", paths: ["src/index.ts", "src/types.ts"], reason: "Working on core" })

// Release when done
pi_messenger({ action: "release" })

Agent Communication

// Rename yourself
pi_messenger({ action: "rename", name: "MyAgentName" })

// Send message to specific agent
pi_messenger({ action: "send", to: "OtherAgent", message: "Hello!" })

// Broadcast to all
pi_messenger({ action: "broadcast", message: "Announcement" })

Messages are logged to the feed and visible in the overlay. DMs interrupt only the target agent (delivered as steering); broadcasts interrupt all agents.

Typical Crew Session

// 1. Join
pi_messenger({ action: "join" })

// 2. Plan (spawns planner agent)
pi_messenger({ action: "plan" })

// 3. Check tasks
pi_messenger({ action: "task.list" })

// 4. Work
pi_messenger({ action: "work", autonomous: true })

// 5. Status
pi_messenger({ action: "status" })

Data Storage

Crew stores data in .pi/messenger/crew/:

.pi/messenger/crew/
├── config.json              # Project config (concurrency, models, coordination, etc.)
├── plan.json                # Plan metadata
├── planning-progress.md     # Planner output log (Notes section for steering)
├── planning-outline.md      # Latest plan outline
├── planning-state.json      # Current planning phase/pass
├── agents/                  # Optional: project-level crew agent overrides
│   └── crew-worker.md
├── tasks/
│   ├── task-1.json          # Task metadata (status, deps, summary)
│   ├── task-1.md            # Task spec (planner-generated)
│   ├── task-1.progress.md   # Worker progress log
│   └── ...
├── blocks/
│   └── task-N.md            # Block context
└── artifacts/               # Debug artifacts (agent input/output)

The activity feed lives at .pi/messenger/feed.jsonl (project-scoped, shared across all agents in the project).

Each crew agent ships with a default model:

AgentRoleDefault Model
crew-plannerplanneranthropic/claude-opus-4-6
crew-workerworkeranthropic/claude-haiku-4-5
crew-reviewerrevieweranthropic/claude-opus-4-6
crew-plan-syncanalystanthropic/claude-haiku-4-5

Override via crew.models.<role> in config. To customize an agent for a project, copy it from ~/.pi/agent/extensions/pi-messenger/crew/agents/ to .pi/messenger/crew/agents/ and edit the frontmatter — project-level agents override extension defaults by name. Agents support thinking: <level> in frontmatter (off, minimal, low, medium, high, xhigh). Config thinking.<role> overrides the frontmatter value.

Configuration

User-level config goes in ~/.pi/agent/pi-messenger.json under a crew key. Project-level config goes in .pi/messenger/crew/config.json. Project overrides user, both override defaults.

Crew spawns multiple LLM sessions in parallel — start with a cheap worker model and scale up. Add this to ~/.pi/agent/pi-messenger.json:

{ "crew": { "models": { "worker": "claude-haiku-4-5" } } }

Model strings accept provider/model format for explicit provider selection and :level suffix for inline thinking control:

{ "crew": { "models": { "worker": "anthropic/claude-haiku-4-5", "planner": "openrouter/anthropic/claude-sonnet-4:high" } } }

The :level suffix and the thinking.<role> config are independent — if both are set, the suffix takes precedence.

Full example (~/.pi/agent/pi-messenger.json):

{
  "crew": {
    "concurrency": { "workers": 3, "max": 6 },
    "models": { "worker": "claude-haiku-4-5", "planner": "claude-sonnet-4-6" },
    "coordination": "chatty",
    "work": { "maxAttemptsPerTask": 5 }
  }
}

Project-level (.pi/messenger/crew/config.json):

{
  "concurrency": { "workers": 4 },
  "coordination": "moderate",
  "models": { "worker": "gpt-4.1" },
  "planning": { "maxPasses": 1 },
  "work": {
    "maxWaves": 50,
    "env": { "OPENAI_API_BASE": "https://custom.endpoint" }
  }
}

Key Settings

SettingDescriptionDefault
concurrency.workersDefault parallel workers per wave2
concurrency.maxMaximum workers allowed via + key (hard ceiling: 10)10
dependenciesDependency scheduling mode: advisory or strict"advisory"
coordinationWorker communication level: none, minimal, moderate, chattychatty
messageBudgetsMax outgoing messages per worker per level (sends rejected after limit){none: 0, minimal: 2, moderate: 5, chatty: 10}
models.workerDefault model for workersanthropic/claude-haiku-4-5
models.plannerDefault model for planneranthropic/claude-opus-4-6
models.reviewerDefault model for revieweranthropic/claude-opus-4-6
models.analystDefault model for analyst (plan-sync)anthropic/claude-haiku-4-5
thinking.plannerThinking level for planner agent(from frontmatter)
thinking.workerThinking level for worker agents(from frontmatter)
thinking.reviewerThinking level for reviewer agents(from frontmatter)
thinking.analystThinking level for analyst agents(from frontmatter)
planning.maxPassesMax planner/reviewer refinement passes1
work.maxWavesMax autonomous waves50
work.maxAttemptsPerTaskMax attempts before auto-blocking a task5
work.stopOnBlockStop autonomous mode when any task blocksfalse
work.shutdownGracePeriodMsGrace period before SIGTERM on abort30000
work.envEnvironment variables passed to spawned workers{}

Coordination Levels

Controls how much workers communicate during execution:

  • none: No coordination instructions. Workers just execute their task.
  • minimal: Check reservations before editing files. Message if conflicts.
  • moderate: Announce start/completion via broadcast. Check reservations. Ask about unclear dependencies.
  • chatty (default): All of moderate, plus: DM peers whose tasks overlap, share progress on interface changes, respond to incoming messages, claim next task after completion.

At moderate and chatty, workers also receive recent activity context and concurrent task info in their prompts.

Model Override Priority

Worker model is resolved with 4-level priority (highest wins):

  1. Per-task model field on the task object
  2. Per-wave model param on the work call
  3. Config-level crew.models.worker
  4. Agent .md frontmatter model field

Graceful Shutdown

When you cancel a work run (Ctrl+C), workers receive a shutdown sequence:

  1. Inbox message asking the worker to stop, release reservations, and exit
  2. Wait shutdownGracePeriodMs (default 30s) for clean exit
  3. SIGTERM if still running, wait 5s
  4. SIGKILL if still running

Tasks from gracefully shutdown workers reset to todo for retry on the next wave. Crashed workers (non-graceful exit) block the task in autonomous mode to prevent retry loops.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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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

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

Codex

37.65%
按下载量换算24

Claude

29.22%
按下载量换算19

Cursor

21.16%
按下载量换算14

Gemini CLI

8.61%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。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。

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