Spawn N subagents that work on the same task in parallel, each in an isolated git worktree.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionspawnExecute the skills CLI command in your project's root directory to begin installation:
Fetches spawn from alirezarezvani/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate spawn. Access via /spawn in your agent's command palette.
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.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
9.7K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
9.7K
stars
Spawn N subagents that work on the same task in parallel, each in an isolated git worktree.
/hub:spawn # Spawn agents for the latest session
/hub:spawn 20260317-143022 # Spawn agents for a specific session
/hub:spawn --template optimizer # Use optimizer template for dispatch prompts
/hub:spawn --template refactorer # Use refactorer template
When --template <name> is provided, use the dispatch prompt from references/agent-templates.md instead of the default prompt below. Available templates:
| Template | Pattern | Use Case |
|---|---|---|
optimizer |
Edit → eval → keep/discard → repeat x10 | Performance, latency, size reduction |
refactorer |
Restructure → test → iterate until green | Code quality, tech debt |
test-writer |
Write tests → measure coverage → repeat | Test coverage gaps |
bug-fixer |
Reproduce → diagnose → fix → verify | Bug fix with competing approaches |
When using a template, replace all {variables} with values from the session config. Assign each agent a different strategy appropriate to the template and task — diverse strategies maximize the value of parallel exploration.
.agenthub/sessions/{session-id}/config.yaml.agenthub/board/dispatch/Agent(
prompt: "You are agent-{i} in hub session {session-id}.
Your task: {task}
Read your full assignment at .agenthub/board/dispatch/{seq}-agent-{i}.md
Instructions:
1. Work in your worktree — make changes, run tests, iterate
2. Commit all changes with descriptive messages
3. Write your result summary to .agenthub/board/results/agent-{i}-result.md
Include: approach taken, files changed, metric if available, confidence level
4. Exit when done
Constraints:
- Do NOT read or modify other agents' work
- Do NOT access .agenthub/board/results/ for other agents
- Commit early and often with descriptive messages
- If you hit a dead end, commit what you have and explain in your result",
isolation: "worktree"
)
running via:python {skill_path}/scripts/session_manager.py --update {session-id} --state running
Tell the user:
/hub:status/hub:evalMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
alirezarezvani/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added spawn from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in spawn — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: spawn is focused, and the summary matches what you get after install.
spawn fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: spawn is focused, and the summary matches what you get after install.
spawn reduced setup friction for our internal harness; good balance of opinion and flexibility.
spawn is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for spawn matched our evaluation — installs cleanly and behaves as described in the markdown.
We added spawn from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
spawn has been reliable in day-to-day use. Documentation quality is above average for community skills.
showing 1-10 of 50