Agent skill

Cross Machine Coordination

by bradygaster in bradygaster/squad

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

MITAuto-check passedDevelopment

Install Cross Machine Coordination

skills CLI
$ npx skills add bradygaster/squad --skill cross-machine-coordination -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bradygaster/squad cross-machine-coordination --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bradygaster/squad.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.squad-templates/skills/cross-machine-coordination .claude/skills/cross-machine-coordination && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cross-machine-coordination
GitHub stars
3.3k
Token cost
~2.7k tokens
SKILL.md length
611 words
Files
1
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 5 steps: Pull the task on next cycle (5-10 min) → Validate schema & command whitelist → Execute the GPU workload → …
  • Development work in your project
  • SKILL.md covers Overview, Usage, File Formats and Security Model, plus 8 more sections
  • Calls git and gh

What it does

Cross Machine Coordination is an agent skill from bradygaster/squad. Enables squad agents on different machines to share work via git-based task queuing

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with Git. The repository describes itself as: Squad: AI agent teams for any project. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “Use the cross-machine-coordination skill to enable squad agents on different machines to share work via git-based task queuing”
  • “/cross-machine-coordination”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 1fd7e03. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cross Machine Coordination loads about 2.7k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 611 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from bradygaster/squad at commit 1fd7e03, republished under its MIT licence (© bradygaster). 611 words, ~2,707 tokens.

Download SKILL.mdSave it as .claude/skills/cross-machine-coordination/SKILL.md (or your agent's skills folder).
name
cross-machine-coordination
description
Enables squad agents on different machines to share work via git-based task queuing
domain
orchestration
confidence
medium
source
manual

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:

bash
# 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):

python
# 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:

bash
# 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:

yaml
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:

yaml
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

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:

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:

yaml
# .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:

bash
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:

yaml
# .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:

bash
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

Show full SKILL.md (258 more words)Show less
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
bash
ls -la .squad/cross-machine/tasks/
cat .squad/cross-machine/tasks/*.yaml | grep -E "^(id|status|target_machine):"
Check Results
bash
ls -la .squad/cross-machine/results/
cat .squad/cross-machine/results/{task-id}.yaml
View Execution History
bash
git log --oneline .squad/cross-machine/ | head -20
Monitor Ralph Cycles
bash
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: the research and documentation owner or Ralph (Work Monitor)

© bradygaster, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .squad-templates/skills/cross-machine-coordination of bradygaster/squad.

Open the folder on GitHubat commit 1fd7e03

Compare with similar skills

Cross Machine Coordination next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Cross Machine Coordination

What does Cross Machine Coordination do?

Enables squad agents on different machines to share work via git-based task queuing. Cross Machine Coordination is an agent skill from bradygaster/squad.

When should I use Cross Machine Coordination?

Cross Machine Coordination fits situations like: development work in your project.

How do I install Cross Machine Coordination in Claude Code?

Run `npx skills add bradygaster/squad --skill cross-machine-coordination -a claude-code`. Or copy the skill folder (.squad-templates/skills/cross-machine-coordination in bradygaster/squad) into .claude/skills/cross-machine-coordination in your project. Claude Code loads it when a task matches its description.

How do I install Cross Machine Coordination in Codex?

Run `npx skills add bradygaster/squad --skill cross-machine-coordination -a codex`. Or copy the skill folder (.squad-templates/skills/cross-machine-coordination in bradygaster/squad) into .agents/skills/cross-machine-coordination in your project. Codex loads it when a task matches its description.

Can I use Cross Machine Coordination in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bradygaster/squad --skill cross-machine-coordination -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cross-machine-coordination, .gemini/skills/cross-machine-coordination, .github/skills/cross-machine-coordination and .opencode/skills/cross-machine-coordination in your project.

What does Cross Machine Coordination need to run?

Going by SKILL.md and its folder, Cross Machine Coordination needs the command-line tools its instructions call (git and gh). Our summary lists: Python 3.

Does Cross Machine Coordination access the network?

SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Cross Machine Coordination safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cross Machine Coordination use?

Cross Machine Coordination is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cross Machine Coordination use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cross Machine Coordination?

Skills that share tags, products or a category with Cross Machine Coordination: Finishing a Development Branch (obra/superpowers, 296k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Codebase Knowledge Graph Q&A (Egonex-AI/Understand-Anything, 86k stars) and Code Design Rationale Investigator (cursor/plugins, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cross Machine Coordination?

bradygaster (a GitHub user) maintains it in bradygaster/squad, which has 3,257 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 8, 2026.

Source: bradygaster/squad on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.