Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· Developer Tools
Hackathon Swarm Coding
Autonomously plans, develops, tests, and delivers full software projects from plain-English prompts using coordinated multi-agent roles and automated quality...
skill openclawclawhub Free
Autonomously plans, develops, tests, and delivers full software projects from plain-English prompts using coordinated multi-agent roles and automated quality...
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- _meta.json, README.md, package.json, SKILL.md, orchestrator.js
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.2
Provenance
- Publisher
- arunnadarasa
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Swarm Coding Skill
Fully autonomous multi-agent software development. Given a plain-English prompt, the swarm designs, implements, tests, and delivers a complete project end-to-end. Core capability: Code generation via OpenRouter's qwen3-coder model. The orchestrator drives a Planner to create a manifest, then executes specialized worker roles (BackendDev, FrontendDev, QA, DevOps, etc.) in dependency order. All code is written to files; no interactive sessions. Important: This skill generates code for review and deployment by the user. It does not make business decisions or operate autonomously in production. The user remains responsible for security, compliance, and operational decisions.
How It Works
Orchestrator (Planner role) analyzes your prompt, decides tech stack and architecture, and creates a swarm.yaml manifest with tasks and dependencies. Worker agents (BackendDev, FrontendDev, QA, DevOps) are spawned as sub-sessions. Each has a clear persona and works on its assigned files in a shared workspace. Coordination: The orchestrator tracks task completion and dependencies. When a task finishes, it marks it done and starts any unblocked downstream tasks. Conflict avoidance: Files are partitioned by role (Backend owns server/, Frontend owns client/, etc.). If two roles need the same file, the manifest assigns an owner. Quality gates: QA must pass tests before integration; DevOps ensures containerization; no merge without green tests. Deliverable: You get a complete project directory with README, tests, Dockerfile, and optionally a GitHub repo or zip.
Usage
# In your main OpenClaw session, invoke: /trigger swarm-code "Build a dashboard that shows Moltbook stats and ClawCredit status" The skill will: Spawn the orchestrator in an isolated session Orchestrator spawns workers sequentially or in parallel (based on dependencies) Output a final summary and path to the completed project
Requirements
Node.js v18+ Environment variables (in .env at workspace root): Required: OPENROUTER_API_KEY β OpenRouter API key with qwen/qwen3-coder access Optional: OPENROUTER_MODEL (default: qwen/qwen3-coder), MOCK=1 for dry-run Internet access for OpenRouter API (and optionally GitHub/Docker if deployment requested) Important: The orchestrator reads .env from the workspace root (parent directory of this skill) and writes project files to swarm-projects/ and logs to .learnings/ in that same workspace root. Run in an isolated workspace to avoid exposing unrelated secrets.
Configuration
Store your OpenRouter key in .env at the workspace root: OPENROUTER_API_KEY=sk-or-... Optional overrides: OPENROUTER_MODEL=qwen/qwen3-coder MOCK=1 # dry-run, no API calls The skill uses qwen/qwen3-coder by default. Ensure your OpenRouter key has that model enabled.
Output
The created project lives in swarm-projects/<timestamp>/ and includes: README.md with run instructions package.json (or equivalent) Source code organized by component test/ directory with automated tests Dockerfile and docker-compose.yml (if applicable) CI/ with GitHub Actions workflow (optional) DECISIONS.md β Project memory documenting key architectural and technical decisions with rationale .learnings/ β Learning logs capturing errors, insights, and feature requests ERRORS.md β Failures, exceptions, and recovery actions LEARNINGS.md β Corrections, better approaches, knowledge gaps FEATURE_REQUESTS.md β Requested capabilities that don't exist yet SWARM_SUMMARY.md β Execution summary with role performance, statistics, and next steps
Continuous Improvement
The swarm skill automatically captures learnings during execution to improve future runs:
What Gets Logged
Worker failures β .learnings/ERRORS.md with context and recovery suggestions Better approaches discovered β .learnings/LEARNINGS.md (e.g., "Simplified X by using Y") User corrections β .learnings/LEARNINGS.md when you override a decision Missing capabilities β .learnings/FEATURE_REQUESTS.md when you ask for something the skill can't do
After Each Run
A SWARM_SUMMARY.md is generated with: Role success/failure rates Total files generated References to learnings captured Recommendations for next steps
Promoting Learnings
Over time, review .learnings/ files: Recurring error patterns β update orchestrator prompts or add retry logic Better approaches β incorporate into the skill's default behavior Feature requests β consider for skill enhancements This creates a feedback loop where each swarm run makes the skill smarter.
Example Prompts
"Build a Node.js API with Express that serves Moltbook stats from JSON logs" "Create a React dashboard with dark theme and charts for ClawCredit status" "Make a CLI tool that checks ClawCredit pre-qualification and notifies via desktop alert" "Generate a smart contract that holds ClawCredit limits and allows x402 payments" "Build a hackathon app: a React dashboard that shows user's token balance using Privy auth" (includes Privy integration out of the box)
Notes
The skill makes all decisions autonomously: tech stack, file structure, library choices. If a task fails, the orchestrator will retry once with adjusted instructions. You can monitor progress via the sub-agent logs in .openclaw/agents/<agent-id>/sessions/. To stop early, send /stop to the orchestrator's session. Privy Integration: When the prompt mentions blockchain, web3, tokens, NFTs, or Privy, the skill automatically includes Privy authentication and wallet infrastructure. Backend includes /auth/callback with JWKS verification and a simulated fallback; frontend integrates @privy-io/react-auth if React is used. For advanced agentic wallet controls, see the Privy Agentic Wallets skill. Project Memory: Each swarm run creates a DECISIONS.md file that documents significant decisions made by the planner and each agent. This serves as long-term knowledge groundingβfuture developers (or the same human weeks later) can understand why certain choices were made. Agents are prompted to explain their technical decisions (e.g., library selection, architecture patterns, security tradeoffs) as part of their output. Enjoy your autonomous coding factory π
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Config1 Scripts
- SKILL.md
- README.md
- orchestrator.js
- _meta.json
- package.json