parallel-agents
Orchestration through Claude Code's built-in Agent Tool
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Installation Guide
How to use parallel-agents on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
parallel-agents
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches parallel-agents from davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate parallel-agents. Access via /parallel-agents in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Native Parallel Agents
Orchestration through Claude Code's built-in Agent Tool
Overview
This skill enables coordinating multiple specialized agents through Claude Code's native agent system. Unlike external scripts, this approach keeps all orchestration within Claude's control.
When to Use Orchestration
✅ Good for:
- Complex tasks requiring multiple expertise domains
- Code analysis from security, performance, and quality perspectives
- Comprehensive reviews (architecture + security + testing)
- Feature implementation needing backend + frontend + database work
❌ Not for:
- Simple, single-domain tasks
- Quick fixes or small changes
- Tasks where one agent suffices
Native Agent Invocation
Single Agent
Use the security-auditor agent to review authentication
Sequential Chain
First, use the explorer-agent to discover project structure.
Then, use the backend-specialist to review API endpoints.
Finally, use the test-engineer to identify test gaps.
With Context Passing
Use the frontend-specialist to analyze React components.
Based on those findings, have the test-engineer generate component tests.
Resume Previous Work
Resume agent [agentId] and continue with additional requirements.
Orchestration Patterns
Pattern 1: Comprehensive Analysis
Agents: explorer-agent → [domain-agents] → synthesis
1. explorer-agent: Map codebase structure
2. security-auditor: Security posture
3. backend-specialist: API quality
4. frontend-specialist: UI/UX patterns
5. test-engineer: Test coverage
6. Synthesize all findings
Pattern 2: Feature Review
Agents: affected-domain-agents → test-engineer
1. Identify affected domains (backend? frontend? both?)
2. Invoke relevant domain agents
3. test-engineer verifies changes
4. Synthesize recommendations
Pattern 3: Security Audit
Agents: security-auditor → penetration-tester → synthesis
1. security-auditor: Configuration and code review
2. penetration-tester: Active vulnerability testing
3. Synthesize with prioritized remediation
Available Agents
| Agent | Expertise | Trigger Phrases |
|---|---|---|
orchestrator |
Coordination | "comprehensive", "multi-perspective" |
security-auditor |
Security | "security", "auth", "vulnerabilities" |
penetration-tester |
Security Testing | "pentest", "red team", "exploit" |
backend-specialist |
Backend | "API", "server", "Node.js", "Express" |
frontend-specialist |
Frontend | "React", "UI", "components", "Next.js" |
test-engineer |
Testing | "tests", "coverage", "TDD" |
devops-engineer |
DevOps | "deploy", "CI/CD", "infrastructure" |
database-architect |
Database | "schema", "Prisma", "migrations" |
mobile-developer |
Mobile | "React Native", "Flutter", "mobile" |
api-designer |
API Design | "REST", "GraphQL", "OpenAPI" |
debugger |
Debugging | "bug", "error", "not working" |
explorer-agent |
Discovery | "explore", "map", "structure" |
documentation-writer |
Documentation | "write docs", "create README", "generate API docs" |
performance-optimizer |
Performance | "slow", "optimize", "profiling" |
project-planner |
Planning | "plan", "roadmap", "milestones" |
seo-specialist |
SEO | "SEO", "meta tags", "search ranking" |
game-developer |
Game Development | "game", "Unity", "Godot", "Phaser" |
Claude Code Built-in Agents
These work alongside custom agents:
| Agent | Model | Purpose |
|---|---|---|
| Explore | Haiku | Fast read-only codebase search |
| Plan | Sonnet | Research during plan mode |
| General-purpose | Sonnet | Complex multi-step modifications |
Use Explore for quick searches, custom agents for domain expertise.
Synthesis Protocol
After all agents complete, synthesize:
## Orchestration Synthesis
### Task Summary
[What was accomplished]
### Agent Contributions
| Agent | Finding |
|-------|---------|
| security-auditor | Found X |
| backend-specialist | Identified Y |
### Consolidated Recommendations
1. **Critical**: [Issue from Agent A]
2. **Important**: [Issue from Agent B]
3. **Nice-to-have**: [Enhancement from Agent C]
### Action Items
- [ ] Fix critical security issue
- [ ] Refactor API endpoint
- [ ] Add missing tests
Best Practices
- Available agents - 17 specialized agents can be orchestrated
- Logical order - Discovery → Analysis → Implementation → Testing
- Share context - Pass relevant findings to subsequent agents
- Single synthesis - One unified report, not separate outputs
- Verify changes - Always include test-engineer for code modifications
Key Benefits
- ✅ Single session - All agents share context
- ✅ AI-controlled - Claude orchestrates autonomously
- ✅ Native integration - Works with built-in Explore, Plan agents
- ✅ Resume support - Can continue previous agent work
- ✅ Context passing - Findings flow between agents
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- AArjun Martinez★★★★★Dec 28, 2024
Registry listing for parallel-agents matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYuki Ramirez★★★★★Dec 28, 2024
Keeps context tight: parallel-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- CChaitanya Patil★★★★★Dec 8, 2024
Keeps context tight: parallel-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDev Jain★★★★★Dec 8, 2024
parallel-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
- NNaina Chawla★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: parallel-agents is focused, and the summary matches what you get after install.
- PPiyush G★★★★★Nov 27, 2024
parallel-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLucas Sanchez★★★★★Nov 27, 2024
Keeps context tight: parallel-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- MMeera White★★★★★Nov 27, 2024
parallel-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- IIsabella Wang★★★★★Nov 19, 2024
Useful defaults in parallel-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAmina Robinson★★★★★Nov 19, 2024
parallel-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
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Discussion
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