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cross-machine-coordination

Plan task handoffs between agents on different machines using Git to share YAML task and result files. This implementation specification describes validation, execution limits, result reporting, and GitHub Issues as an additional route for urgent requests.

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Enables squad agents on different machines to share work via git-based task queuing

集成工具GitHubAzure
查看安装方法

适用场景

  • Designing GPU task handoffs from a laptop to a DevBox
  • Standardizing task and result files across machines
  • Designing urgent requests through labeled GitHub Issues
  • Planning timeout handling and retries for remote tasks

包含的文件(1)

  • SKILL.md10.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Skill: Cross-Machine Coordination Pattern

Skill ID: cross-machine-coordination
Owner: Ralph (Work Monitor)
Squad Integration: All agents
Status: Specification (ready for implementation)


Overview

Enables squad agents running on different machines (laptop, DevBox, Azure VM) to securely share work, coordinate execution, and pass results without manual intervention.

Pattern: Git-based task queuing + GitHub Issues supplement


Usage

For Task Sources (Orchestrating Machine)

To assign work to DevBox:

# Create task file
cat > .squad/cross-machine/tasks/2026-03-14T1530Z-laptop-gpu-voice-clone.yaml << 'EOF'
id: gpu-voice-clone-001
source_machine: laptop-machine
target_machine: devbox
priority: high
created_at: 2026-03-14T15:30:00Z
task_type: gpu_workload
payload:
  command: "python scripts/voice-clone.py --input voice.wav --output cloned.wav"
  expected_duration_min: 15
  resources:
    gpu: true
    memory_gb: 8
status: pending
EOF

# Commit & push
git add .squad/cross-machine/tasks/
git commit -m "Cross-machine task: GPU voice cloning [squad:machine-devbox]"
git push origin main

Ralph on DevBox will:

  1. Pull the task on next cycle (5-10 min)
  2. Validate schema & command whitelist
  3. Execute the GPU workload
  4. Write result to .squad/cross-machine/results/gpu-voice-clone-001.yaml
  5. Commit & push the result

For Task Executors (DevBox, Azure VMs)

Ralph automatically watches .squad/cross-machine/tasks/ for work targeted at this machine.

On each cycle (5-10 min):

# Pseudo-code (Ralph implementation)
1. git pull origin main
2. Load all .yaml files in .squad/cross-machine/tasks/
3. Filter for status=pending AND target_machine=HOSTNAME
4. For each task:
   a. Validate schema (must have: id, source_machine, target_machine, payload)
   b. Validate command against whitelist
   c. Execute task (with timeout)
   d. Write result to .squad/cross-machine/results/{id}.yaml
   e. Commit & push result

For Urgent/Ad-Hoc Tasks

Use GitHub Issues with squad:machine-{name} label:

# Create issue
gh issue create \
  --title "GPU: Clone voice profile from sample.wav" \
  --body "Execute voice cloning on DevBox. Input: /path/to/voice-input.wav" \
  --label "squad:machine-devbox" \
  --label "urgent"

Ralph on DevBox will:

  1. Detect issue with squad:machine-devbox label
  2. Parse task from issue body
  3. Execute task
  4. Comment with result
  5. Close issue

File Formats

Task File (YAML)

Location: .squad/cross-machine/tasks/{timestamp}-{machine}-{task-id}.yaml

Required Fields:

id: {task-id}                      # Unique identifier (alphanumeric + dash)
source_machine: {hostname}         # Where task was created
target_machine: {hostname}         # Where task will execute
priority: high|normal|low          # Execution priority
created_at: 2026-03-14T15:30:00Z   # ISO 8601 timestamp
task_type: gpu_workload|script|... # Category
payload:
  command: "..."                   # Shell command to execute
  expected_duration_min: 15        # Timeout (minutes)
  resources:
    gpu: true|false
    memory_gb: 8
    cpu_cores: 4
status: pending|executing|completed|failed

Optional Fields:

description: "Human-readable task description"
timeout_override_min: 120          # Override default timeout
retry_count: 3                     # Retry failed tasks

Result File (YAML)

Location: .squad/cross-machine/results/{task-id}.yaml

id: {task-id}                          # Links back to task
target_machine: devbox                 # Executed on
completed_at: 2026-03-14T15:45:00Z    # When it finished
status: completed|failed|timeout       # Outcome
exit_code: 0                           # Shell exit code
stdout: "..."                          # Captured output
stderr: "..."                          # Captured errors
duration_seconds: 900                  # How long it took
artifacts:
  - path: "/path/to/artifacts/..."   # Location of results
    type: audio|text|model|...
    size_mb: 2.5

Security Model

Validation Pipeline

All tasks go through:

  1. Schema Validation

    • YAML structure matches spec
    • Required fields present
    • No unexpected fields (reject)
  2. Command Whitelist

    • Only approved commands allowed
    • Path validation (no ../../ escapes)
    • Environment variable sanitization
    • No inline shell operators (&&, |, >)
  3. Resource Limits

    • Timeout enforced (default: 60 min)
    • Memory cap: 16GB (adjustable)
    • CPU threads: 4 (adjustable)
    • Disk write: 100GB (adjustable)
  4. Execution Isolation

    • Runs as unprivileged user
    • Temp directory cleaned after execution
    • Network access: read-only (no outbound writes)
  5. Audit Trail

    • All executions logged to git
    • Commit signed with Ralph's key
    • Result stored immutably

Threat Mitigations

ThreatMitigation
Malicious task injectionBranch protection + PR review before merge
Credential leakagePre-commit secret scan + environment scrubbing
Resource exhaustionTimeout + memory limits
Code injectionCommand whitelist + no shell evaluation
Result tamperingGit commit history is immutable

Configuration

Ralph reads config from .squad/config.json:

{
  "cross_machine": {
    "enabled": true,
    "poll_interval_seconds": 300,
    "this_machine": "devbox",
    "max_concurrent_tasks": 2,
    "task_timeout_minutes": 60,
    "command_whitelist": [
      "python scripts/voice-clone.py",
      "python scripts/data-process.py",
      "bash scripts/cleanup.sh"
    ],
    "result_ttl_days": 30
  }
}

Examples

Example 1: GPU Voice Cloning (Laptop → DevBox)

1. Laptop creates task:

# .squad/cross-machine/tasks/2026-03-14T1530Z-laptop-gpu-001.yaml
id: gpu-voice-clone-001
source_machine: laptop-machine
target_machine: devbox
priority: high
created_at: 2026-03-14T15:30:00Z
task_type: gpu_workload
payload:
  command: "python scripts/voice-clone.py --input voice.wav --output cloned.wav"
  expected_duration_min: 15
  resources:
    gpu: true
    memory_gb: 8
status: pending

2. Laptop commits & pushes:

git add .squad/cross-machine/tasks/
git commit -m "Task: GPU voice cloning [squad:machine-devbox]"
git push origin main

3. DevBox Ralph (5 min later):

[Ralph Watch Cycle]
- Pulled origin/main
- Detected: gpu-voice-clone-001 (status: pending, target: devbox)
- Validation: ✅ Schema OK, command whitelisted
- Executing: python scripts/voice-clone.py ...
- [15 minutes of processing]
- Completed: exit code 0
- Writing result...
- Committing & pushing...

4. Laptop Ralph (next cycle) sees result:

# .squad/cross-machine/results/gpu-voice-clone-001.yaml
id: gpu-voice-clone-001
target_machine: devbox
completed_at: 2026-03-14T15:45:00Z
status: completed
exit_code: 0
stdout: "Voice cloning completed. Output written to /tmp/cloned.wav"
stderr: ""
duration_seconds: 900
artifacts:
  - path: "/path/to/artifacts/voice-clone-001/output.wav"
    type: audio
    size_mb: 2.5

Example 2: Urgent Debug Request (Human → DevBox via Issue)

Create issue:

gh issue create \
  --title "DevBox: Debug voice model failure" \
  --body "Error: Model failed to load on last run. Please check /tmp/model.log and report findings." \
  --label "squad:machine-devbox" \
  --label "urgent"

DevBox Ralph detects → executes → comments:

✅ Executed on devbox at 2026-03-14 15:47:00
Command: python scripts/debug-model.py

Result:
------
Model file: /tmp/model-v2.bin (OK)
Checksum: a1b2c3d4e5f6 (matches expected)
Memory available: 12 GB (sufficient)

ERROR FOUND: Config file permission issue
  - File: ~/.config/voice/model.yaml
  - Permissions: -rw------- (owner-only)
  - Expected: -rw-r--r-- (world-readable for service)

FIX: Run: chmod 644 ~/.config/voice/model.yaml

Error Handling

Task Execution Failures

If a task fails (exit code != 0):

  1. Result written with status: failed + exit code
  2. stderr captured in result
  3. Committed to git for audit
  4. Source machine can retry by re-pushing task with status: pending

Stalled Tasks

If a task doesn't complete within timeout:

  1. Process killed
  2. Result written with status: timeout
  3. stderr: "Execution exceeded X minutes"
  4. Source can investigate or retry

Network Failures

If git push/pull fails:

  • Ralph retries on next cycle
  • Tasks queue locally until connectivity restored
  • No tasks lost (stored in local repo)

Monitoring & Debugging

Check Task Queue

ls -la .squad/cross-machine/tasks/
cat .squad/cross-machine/tasks/*.yaml | grep -E "^(id|status|target_machine):"

Check Results

ls -la .squad/cross-machine/results/
cat .squad/cross-machine/results/{task-id}.yaml

View Execution History

git log --oneline .squad/cross-machine/ | head -20

Monitor Ralph Cycles

tail -f .squad/log/ralph-watch.log | grep "cross-machine"

Integration with Ralph Watch

Ralph automatically includes this pattern in its watch loop:

Ralph Watch Cycle (every 5-10 min):
1. Fetch GitHub issues with squad:machine-* labels
2. Poll .squad/cross-machine/tasks/
3. For each matching task:
   - Validate
   - Execute
   - Write result
   - Commit & push
4. Update status in issue (if applicable)
5. Sleep until next cycle

No manual Ralph configuration needed — just create task files or issues with the right labels.


Migration from Manual Handoff

Before (today):

  • Laptop → user manually copies file to Teams chat
  • user pastes into target terminal
  • user copies output back
  • user pastes result manually

After (with this pattern):

  • Laptop Ralph writes task file → git push
  • DevBox Ralph auto-executes → git push result
  • Laptop Ralph auto-reads result
  • 0 human intervention needed

Future Enhancements

Potential expansions (Phase 2+):

  1. Task Priorities: Execution order based on priority field
  2. Serial Pipelines: Machine A → B → C task chains
  3. GPU Availability Polling: Query DevBox before submitting work
  4. Cost Tracking: Log resource usage per task
  5. Notification Webhooks: Alert on task completion
  6. Web Dashboard: Real-time task status visualization

Questions?

Refer to research report: research/active/cross-machine-agents/README.md

Contact: Seven (Research & Docs) or Ralph (Work Monitor)

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