Model Queue Skill

A multi-queue task orchestration system where tasks are routed to queues based on their target model source. Supports task dependencies, context passing between tasks, and configurable failure handling.

Core Concepts

Model Source Mapping

Models are grouped by their underlying source (e.g., local Ollama, remote Ollama, cloud API). All tasks targeting models on the same source share a single queue.

Configuration in TOOLS.md:

MODEL_SOURCE_OLLAMA_LOCAL=ollama/llama3,ollama/qwen2.5,ollama/mistral
MODEL_SOURCE_OLLAMA_REMOTE=ollama-remote/qwen3.5:27b,ollama-remote/llama3:70b
MODEL_SOURCE_CLOUD_NVIDIA=nvidia/z-ai/glm5,nvidia/llama3

Queue Isolation

Each model source has its own queue file:

• ${MODEL_QUEUES_DIR}/ollama-local.json
• ${MODEL_QUEUES_DIR}/ollama-remote.json

Queues are independent — a blocked task in one queue doesn't affect other queues.

Task Object Schema

{
  "id": "T-001",
  "queue": "ollama-local",
  "model": "ollama/qwen2.5",
  "description": "Analyze the sales data",
  "goal": "Generate a summary of Q1 sales performance",
  "status": "pending",
  "priority": 0,
  "depends_on": null,
  "on_depends_fail": "block",
  "context_input": null,
  "result": null,
  "result_status": null,
  "result_summary": null,
  "retries": 0,
  "maxRetries": 3,
  "subagent_session": null,
  "added_at": "2026-03-04T16:51:00Z",
  "started_at": null,
  "completed_at": null
}

Task Statuses

Two Operating Modes

A1 — Triggers

Model Source Configuration

REQUIRED: Configure model sources in TOOLS.md before first use.

Add to your TOOLS.md:

## Model Queue Configuration

MODEL_QUEUES_DIR=~/.openclaw/model-queues/
MODEL_QUEUE_MAX_RETRIES=3
MODEL_QUEUE_ARCHIVE_DAYS=7

# Model Source Mappings (REQUIRED)
# Format: MODEL_SOURCE_{NAME}=model1,model2,model3
MODEL_SOURCE_OLLAMA_LOCAL=ollama/llama3,ollama/qwen2.5,ollama/mistral
MODEL_SOURCE_OLLAMA_REMOTE=ollama-remote/qwen3.5:27b,ollama-remote/llama3:70b
MODEL_SOURCE_CLOUD_NVIDIA=nvidia/z-ai/glm5,nvidia/llama3

# Default model source when not specified (optional)
DEFAULT_MODEL_SOURCE=ollama-remote

How Model Source is Determined

1. Explicit model: User says "用 qwen2.5 分析..." → find qwen2.5's source
2. Explicit source: User says "send to remote..." → use default model for that source
3. No specification: Use DEFAULT_MODEL_SOURCE, or ask user

INTAKE Triggers

Activate INTAKE mode when user message matches:

Do NOT activate INTAKE for:

• Simple one-sentence questions
• Pure scheduling requests without tasks
• Single web searches

DISPATCHER Triggers

• Immediately after INTAKE (primary path)
• HEARTBEAT.md check during heartbeat poll (backup)
• systemEvent: "MODEL_QUEUE_DISPATCH: check queues and run pending tasks" (backup)

Mode 1: INTAKE — Step-by-Step

Step 0 — First Run Setup

Run this check before anything else, every INTAKE invocation:

CHECK whether ${MODEL_QUEUES_DIR}/ exists and has queue files
IF no queue files exist:
  → This is the first run. Auto-configure.
  
  [1] Create directory:
      exec: mkdir -p ${MODEL_QUEUES_DIR}
  
  [2] For each MODEL_SOURCE_* in TOOLS.md:
      CREATE ${MODEL_QUEUES_DIR}/{source_name}.json:
      {
        "version": "1.0",
        "source": "{source_name}",
        "models": [list of models],
        "maxConcurrent": 1,
        "maxRetries": ${MODEL_QUEUE_MAX_RETRIES},
        "lastId": null,
        "tasks": []
      }
  
  [3] Register heartbeat entry:
      READ HEARTBEAT.md
      IF "Model Queue Dispatcher" NOT in file:
        APPEND:
        ## Model Queue Dispatcher
        Every heartbeat: check ${MODEL_QUEUES_DIR} for pending/running tasks
        - If tasks exist → run DISPATCHER mode
        - If nothing pending → HEARTBEAT_OK
  
  [4] Register backup cron job:
      CALL cron tool:
        action: "add"
        job:
          name: "Model Queue Dispatcher"
          schedule: { kind: "every", everyMs: 900000 }
          payload: { kind: "systemEvent", text: "MODEL_QUEUE_DISPATCH: check queues and run pending tasks" }
          sessionTarget: "main"
          enabled: true
  
  [5] Notify user:
      "⚙️ Model Queue initialized. N queues ready."
  
  → THEN continue with normal INTAKE steps.

IF queue files already exist:
  → Skip Step 0. Proceed to Step 1.

Step 1 — Parse User Message

Parse the message to extract:

1. Tasks: Split by numbered lists, bullets, "然后", "and then", "next"
2. Model/Source: Look for model names or source keywords
3. Dependencies: "然后", "after that", "based on that" implies dependency on previous task
4. Control commands: "cancel T-XXX", "retry T-XXX", "show status"

Step 2 — Handle Control Commands

Step 3 — Determine Model Source

For each task:

IF user specified model explicitly (e.g., "用 qwen2.5"):
  FIND model in MODEL_SOURCE_* configs
  SET task.model = specified model
  SET task.queue = source containing that model

ELSE IF user specified source (e.g., "用远程", "send to remote"):
  FIND matching MODEL_SOURCE_*
  SET task.queue = that source
  SET task.model = first model in that source (or ask)

ELSE:
  IF DEFAULT_MODEL_SOURCE configured:
    SET task.queue = DEFAULT_MODEL_SOURCE
    SET task.model = first model in that source
  ELSE:
    ASK user: "Which model/source should handle this task?"

Step 4 — Determine Dependencies

IF task contains dependency indicators ("然后", "after that", "based on that"):
  IF previous task in same message:
    SET task.depends_on = previous_task.id
    SET task.on_depends_fail = "block" (default)
  ELSE:
    ASK user: "This task depends on which previous task?"

IF user explicitly specified dependency ("depends on T-XXX"):
  SET task.depends_on = specified_id

Step 5 — Assign Task ID

READ queue file for task.queue
IF queue.lastId is null:
  task.id = "T-001"
ELSE:
  PARSE number from queue.lastId (e.g., "T-005" → 5)
  INCREMENT number
  FORMAT as T-NNN (e.g., 6 → "T-006")
  task.id = formatted ID

Step 6 — Build Task Object

Create task object with:

• id, queue, model, description, goal
• status: "pending" if no dependency, "waiting" if has dependency
• priority: 0 (default) or user-specified
• depends_on, on_depends_fail
• retries: 0, maxRetries: from config
• added_at: current timestamp
• All other fields: null

Step 7 — Append to Queue

READ queue file for task.queue
APPEND task to queue.tasks[]
UPDATE queue.lastId = task.id
WRITE queue file

Step 8 — Confirm to User

Single task:

📋 Added T-001 to queue [ollama-local]
Model: ollama/qwen2.5
Task: Analyze the sales data
Queue position: 1

Multiple tasks:

📋 Added 3 tasks to queue [ollama-local]:
• T-001: Analyze the sales data
• T-002: Generate report (depends on T-001)
• T-003: Send to team (depends on T-002)

Starting dispatcher now...

Step 9 — Run DISPATCHER

Immediately after confirming, run DISPATCHER mode (see below).

Do not exit and wait for heartbeat. Tasks must start executing immediately.

Mode 2: DISPATCHER — Step-by-Step

Step 1 — Scan All Queue Files

LIST all *.json files in ${MODEL_QUEUES_DIR}/
FOR EACH queue file:
  READ queue data

Step 2 — Check Running Tasks

For each queue:

running_tasks = tasks where status = "running"

FOR EACH running_task:
  IF running_task.subagent_session is set:
    CALL subagents(action="list") to check session status
    
    IF session is DONE:
      READ result from session
      SET running_task.status = "done"
      SET running_task.result = full result
      SET running_task.result_summary = extract summary
      SET running_task.completed_at = now()
      NOTIFY user: "✅ {running_task.id} done — {summary}"
    
    ELSE IF session is FAILED or ERROR:
      IF running_task.retries < running_task.maxRetries:
        INCREMENT running_task.retries
        SET running_task.status = "pending"
        LOG: "Retrying {running_task.id} (attempt {retries+1})"
      ELSE:
        SET running_task.status = "failed"
        SET running_task.completed_at = now()
        NOTIFY user: "❌ {running_task.id} failed after {retries} attempts"
    
    ELSE IF session is STILL RUNNING:
      LEAVE as-is (check again next heartbeat)

Step 3 — Check Waiting Tasks

For each task with status = "waiting":

IF task.depends_on is set:
  FIND dependency task in ANY queue (cross-queue lookup allowed)
  
  IF dependency.status == "done":
    SET task.status = "pending"
    SET task.context_input = {
      "source_task": dependency.id,
      "result_summary": dependency.result_summary,
      "result_status": dependency.result_status,
      "included_at": now()
    }
    LOG: "{task.id} dependency satisfied, ready to run"
  
  ELSE IF dependency.status in ["failed", "blocked"]:
    SWITCH task.on_depends_fail:
      CASE "block":
        SET task.status = "blocked"
        SET task.blocked_reason = "Dependency {dependency.id} failed"
        NOTIFY user: "⚠️ {task.id} blocked — dependency {dependency.id} failed"
      
      CASE "skip":
        SET task.status = "skipped"
        SET task.skipped_reason = "Dependency {dependency.id} failed"
        NOTIFY user: "⏭️ {task.id} skipped — dependency {dependency.id} failed"
      
      CASE "continue":
        SET task.status = "pending"
        SET task.context_input = {
          "warning": "Dependency {dependency.id} failed",
          "included_at": now()
        }
        LOG: "{task.id} continuing despite failed dependency"
  
  ELSE IF dependency.status in ["pending", "waiting", "running"]:
    LEAVE task.status = "waiting"

Step 4 — Dispatch Pending Tasks

For each queue:

pending_tasks = tasks where status = "pending"
running_count = count of tasks where status = "running"

IF running_count >= queue.maxConcurrent:
  SKIP this queue (no slots available)

ELSE:
  slots_available = queue.maxConcurrent - running_count
  
  SORT pending_tasks by:
    1. priority (DESC)
    2. added_at (ASC)
  
  FOR EACH pending_task, up to slots_available:
    DISPATCH task:
      [1] BUILD subagent prompt:
          "You are executing task {task.id} for model-queue.
          
          Target Model: {task.model}
          Task: {task.description}
          Goal: {task.goal}
          
          {% if task.context_input %}
          Context from previous task {task.context_input.source_task}:
          {task.context_input.result_summary}
          {% endif %}
          
          Execute this task. When complete, report:
          1. The result (full output)
          2. A brief summary (1-2 sentences)  
          3. Status: success, partial, or failed
          4. If failed, explain why and suggest what might help
          
          Focus only on this task. Do not start other tasks."
      
      [2] CALL sessions_spawn:
          task: subagent prompt
          model: task.model (or default for queue)
          mode: "run"
          timeoutSeconds: appropriate timeout
      
      [3] UPDATE task:
          SET task.status = "running"
          SET task.subagent_session = spawned session ID
          SET task.started_at = now()

Step 5 — Save All Queues

FOR EACH modified queue file:
  WRITE queue data to disk

Step 6 — Return Appropriate Response

IF any notifications were sent (done/failed/blocked):
  RETURN the notifications (active heartbeat response)

ELSE IF tasks were dispatched:
  RETURN "Dispatched N tasks across M queues."

ELSE IF nothing to do (no pending, no running):
  RETURN "HEARTBEAT_OK"

Output Templates

Task Added

📋 Added {task.id} to queue [{queue}]
Model: {task.model}
{task.description}
{% if task.depends_on %}
Depends on: {task.depends_on} (block on failure)
{% endif %}
Queue position: {position}

Task Completed

✅ {task.id} done — {task.result_summary}
Queue: {task.queue} | Duration: {duration}

Task Failed

❌ {task.id} failed after {task.retries} attempts
Queue: {task.queue}
Error: {task.error_message}

To retry: "retry {task.id}"

Queue Status

📊 Queue Status

[{source-1}] {pending} pending, {running} running, {done} done
  {T-XXX} 🔄 running: {description} (started {time} ago)
  {T-XXX} ⏳ pending: {description}
  {T-XXX} ⏳ waiting: {description} (depends on {T-YYY})
  {T-XXX} 🚫 blocked: {description} — {blocked_reason}

[{source-2}] {pending} pending, {running} running, {done} done
  (empty)

A5 — Configuration Reference

TOOLS.md Settings

Queue File Structure

{
  "version": "1.0",
  "source": "ollama-local",
  "models": ["ollama/llama3", "ollama/qwen2.5"],
  "maxConcurrent": 1,
  "maxRetries": 3,
  "lastId": "T-005",
  "tasks": [...]
}

A6 — Heartbeat Integration

Heartbeat and cron setup is automatic. Step 0 of INTAKE mode handles this.

HEARTBEAT.md Entry (auto-injected)

## Model Queue Dispatcher

Every heartbeat: check ${MODEL_QUEUES_DIR} for pending/running tasks
- If tasks exist → run DISPATCHER mode (model-queue skill)
- If nothing pending → HEARTBEAT_OK

Backup Cron Job (auto-registered)

every 15 min → systemEvent: "MODEL_QUEUE_DISPATCH: check queues and run pending tasks"
sessionTarget: main

A7 — Edge Cases

A8 — File Organization

${MODEL_QUEUES_DIR}/
  ollama-local.json
  ollama-remote.json
  cloud-nvidia.json
  archive/
    ollama-local/
      2026-03.json
    ollama-remote/
      2026-03.json

Future Enhancements (v2)

• Multiple dependencies per task
• Task groups (batch operations)
• Priority boost for urgent tasks
• Queue pause/resume
• Task result caching
• Cross-queue result sharing
• Scheduled task execution (run at specific time)
• Task templates (reusable task definitions)