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pi-messenger-crew

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Use pi-messenger for multi-agent coordination and Crew task orchestration. Covers joining the mesh, planning from PRDs, working on tasks, file reservations, and agent messaging. Load this skill when using pi_messenger or building with Crew.

167 stars
3.3k downloads
Updated 2/9/2026

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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.

Install

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Requires askill CLI v1.0+

AI Quality Score

88/100Analyzed 2/22/2026

Comprehensive technical reference for pi-messenger multi-agent coordination. Well-structured with clear TypeScript examples, configuration tables, and workflow sections. Covers all major features including mesh joining, task planning, work execution, file reservations, and agent messaging. Located in proper skills folder with good metadata. Slight reduction for tool-specific narrow focus but otherwise excellent reference content.

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Metadata

Licenseunknown
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Updated2/9/2026
Publishernicobailon

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