parallel-agents
Use when parallelizing development, running multiple agents, splitting work across agents, coordinating parallel tasks, or decomposing PRDs for concurrent execution. Breaks work into independent agent workstreams.
Best use case
parallel-agents is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when parallelizing development, running multiple agents, splitting work across agents, coordinating parallel tasks, or decomposing PRDs for concurrent execution. Breaks work into independent agent workstreams.
Teams using parallel-agents should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/parallel-agents/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How parallel-agents Compares
| Feature / Agent | parallel-agents | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when parallelizing development, running multiple agents, splitting work across agents, coordinating parallel tasks, or decomposing PRDs for concurrent execution. Breaks work into independent agent workstreams.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Parallel Agent Development
Orchestrate massively parallel development by decomposing work into independent tasks that multiple Claude Code instances can execute simultaneously.
## CLI Tool: cpo (Claude Parallel Orchestrator)
The `cpo` CLI tool handles parallel agent execution with git worktree isolation.
### Installation
```bash
pip install claude-parallel-orchestrator
# or
pipx install claude-parallel-orchestrator
```
### cpo Commands
| Command | Description |
|---------|-------------|
| `cpo init <dir> -t <tech-spec> -n <name>` | Initialize parallel directory |
| `cpo validate <dir>` | Validate manifest and structure |
| `cpo run <dir>` | Execute parallel agents |
| `cpo status <dir>` | Check execution status |
## Workflow Overview
```
/parallel-setup -> One-time: creates parallel/ directory
|
/parallel-decompose -> Per Tech Spec: creates TS-XXXX-slug/ with all artifacts
|
/parallel-run -> Delegates to `cpo run` for execution
|
/parallel-integrate -> Verify & generate integration report
```
## Directory Structure
Each decomposition creates an isolated artifact folder keyed by Tech Spec:
```
project/
parallel/ # Created by /parallel-setup (one-time)
README.md
.gitignore
TS-XXXX-{slug}/ # Created by /parallel-decompose
manifest.json # Regeneration metadata
context.md # Shared project context (token-efficient)
architecture.md # System design from Tech Spec
task-graph.md # Dependency visualization (Mermaid)
contracts/
types.py (or types.ts) # Shared domain types
api-schema.yaml # OpenAPI specification
tasks/
task-001-users.md # Compact YAML format
task-002-products.md
...
prompts/
agent-prompts.md # All launch commands
task-*.txt # Individual agent prompts
integration-report.md # Post-execution report
tech-specs/ # Source Tech Specs
approved/TS-XXXX-slug.md
CLAUDE.md # Project conventions
```
## Related Skills
This skill is part of a family of parallel development skills:
| Skill | Purpose |
|-------|---------|
| **parallel-decompose** | PRD decomposition workflow, task generation, contracts |
| **parallel-prompt-generator** | Generate agent prompts from task specs |
| **parallel-execution** | Git worktrees, parallel execution patterns, scripts |
| **parallel-task-format** | Task spec YAML format, scope notation, agent selection |
| **agent-tools** | Tool permissions, CLI syntax for agent restrictions |
## Quick Start
### Phase 1: Setup (One-Time)
```bash
/parallel-setup --tech django
```
Creates `parallel/` directory structure.
### Phase 2: Decomposition
```bash
/parallel-decompose docs/prd.md --tech-spec tech-specs/approved/TS-0042-inventory.md
```
Creates `parallel/TS-0042-inventory-system/` with:
- manifest.json, context.md, architecture.md
- contracts/ (types.py, api-schema.yaml)
- tasks/ (compact YAML task specs)
- prompts/ (agent launch commands)
### Phase 3: Execution
```bash
# Using /parallel-run (delegates to cpo)
/parallel-run parallel/TS-0042-inventory-system/
# Or using cpo directly
cpo run parallel/TS-0042-inventory-system/
```
### Phase 4: Integration
```bash
/parallel-integrate --parallel-dir parallel/TS-0042-inventory-system
```
Checks contract compliance, boundary compliance, runs tests, generates report.
## manifest.json Format
```json
{
"tech_spec_id": "TS-0042",
"name": "inventory-system",
"technology": "python",
"python_version": "3.11",
"dependencies": {
"python": {
"add": ["pydantic==2.5.3", "sqlalchemy[asyncio]==2.0.25"],
"upgrade": [],
"remove": [],
"add_dev": ["pytest==7.4.3", "pytest-asyncio==0.21.1"]
}
},
"waves": [
{
"number": 1,
"tasks": [
{ "id": "task-001", "agent": "python-experts:django-expert" },
{ "id": "task-002", "agent": "python-experts:django-expert" }
],
"validation": "from apps.users.models import User; print('Wave 1 OK')"
},
{
"number": 2,
"tasks": [
{ "id": "task-003", "agent": "python-experts:django-expert" }
],
"validation": "from apps.orders.models import Order; print('Wave 2 OK')"
}
],
"metadata": {
"tech_spec": "tech-specs/approved/TS-0042-inventory.md",
"generated_at": "2025-01-15T10:00:00Z",
"total_tasks": 3,
"max_parallel": 2,
"critical_path": ["task-001", "task-003"]
}
}
```
### Dependencies Section
The `dependencies` section declares packages to install before task execution. Versions are **pinned** (resolved during `parallel-decompose` using the `dependency-alignment` skill) to ensure reproducibility and avoid conflicts between parallel agents.
```json
{
"dependencies": {
"python": {
"add": ["pydantic==2.5.3"],
"upgrade": ["requests==2.31.0"],
"remove": ["deprecated-lib"],
"add_dev": ["pytest==7.4.3"]
}
}
}
```
| Field | Description | uv Command |
|-------|-------------|------------|
| `add` | Packages to add (if not present) | `uv add <packages>` |
| `upgrade` | Packages to upgrade to specified version | `uv add --upgrade <packages>` |
| `remove` | Packages to remove from project | `uv remove <packages>` |
| `add_dev` | Dev-only packages to add | `uv add --dev <packages>` |
**Execution order:** remove → upgrade → add → add_dev
**Commit strategy:** Dependencies are installed and committed to the feature branch before any task execution begins. This ensures all parallel tasks have access to the same dependencies without conflicts.
**Minimal example** (add only):
```json
{
"dependencies": {
"python": {
"add": ["pydantic==2.5.3"]
}
}
}
```
All fields are optional. Omit sections you don't need (except versions must be pinned).
## Best Practices
- **Spend time on decomposition**: Good decomposition is the multiplier
- **Contract-first**: Interfaces upfront prevent 80% of integration issues
- **Explicit boundaries**: Tell agents what they *cannot* touch
- **Small tasks**: Prefer more, smaller tasks (2-4 hours each)
- **Tech Spec first**: Create a Tech Spec before decomposition
## Anti-Patterns
- Tasks that share mutable state
- Circular dependencies between tasks
- Vague scope boundaries
- Missing contract definitions
- Skipping the integration phase
## Related Commands
| Command | Purpose |
|---------|---------|
| `/parallel-setup` | One-time project initialization |
| `/parallel-decompose` | Per-spec decomposition with prompts |
| `/parallel-run` | Execute and monitor parallel agents |
| `/parallel-integrate` | Post-execution verification |
| `/create-tech-spec` | Create Tech Spec before decomposition |Related Skills
parallel-ready-django
Audit and prepare a Django codebase for parallel multi-agent development. Use when asked to check if a Django project is ready for parallelization, prepare a repo for multi-agent work, audit codebase structure, set up orchestration infrastructure, or identify blockers for parallel development. Analyzes Django apps, models, migrations, and module boundaries.
parallel-fix-django
Fix Django-specific blockers identified in parallelization readiness assessment
parallel-validate-prompts
Validate and fix parallel prompts for required sections
parallel-task-format
Compact YAML format for defining parallel task specifications with scope, boundaries, and agent assignments. Use when creating task files for parallel development.
parallel-setup
One-time setup of parallel/ directory for multi-agent development
parallel-run
Orchestrate parallel agent execution with git worktrees
parallel-prompt-generator
Generate agent-ready prompts from existing task specification files. Use when regenerating prompts after editing tasks, updating prompt templates, or preparing tasks for cpo execution.
parallel-integrate
Verify integration after parallel agent execution and generate report
parallel-execution
Execute multiple Claude Code agents in parallel using the cpo CLI tool. Use when running parallel tasks, monitoring execution, or understanding the execution workflow.
parallel-decompose
Decompose PRDs and Tech Specs into parallel-executable tasks with contracts, prompts, and dependency graphs. Use when breaking down a PRD for multi-agent execution.
zod
Zod schema validation patterns and type inference. Auto-loads when validating schemas, parsing data, validating forms, checking types at runtime, or using z.object/z.string/z.infer in TypeScript.
typescript-import-style
Merge-friendly import formatting (one-per-line, alphabetical). Auto-loads when writing TypeScript/JavaScript imports to minimize merge conflicts in parallel development. Enforces consistent grouping and sorting.