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Hackathon Swarm Coding
作者
Arun Nadarasa
· GitHub ↗
· v0.1.2
766
总下载
3
收藏
0
当前安装
3
版本数
在 OpenClaw 中安装
/install swarm-coding-skill
功能描述
Autonomously plans, develops, tests, and delivers full software projects from plain-English prompts using coordinated multi-agent roles and automated quality...
安全使用建议
Before installing or running this skill:
- Treat it as requiring an OpenRouter API key (OPENROUTER_API_KEY). The registry listing omitted this — verify the key and its model access.
- Run the skill only inside a clean, isolated workspace directory (no other .env or secret files there). The orchestrator reads .env from the workspace root and will throw if missing; if your workspace .env contains other secrets, they could be read by the skill.
- Use MOCK=1 for a dry run to see behavior without API calls.
- Expect generated code and logs (swarm-projects/, DECISIONS.md, .learnings/) to contain your prompts and agent reasoning; review and remove sensitive content before sharing or committing to VCS.
- Pay special attention to any blockchain/Privy integration the skill auto-includes — review auth-related code and never paste real private keys or secrets into prompts.
- If you want to reduce risk, run the skill inside a disposable container/VM or dedicated OS user directory, and ensure .env contains only the OpenRouter key you intend to share. Remove or rotate the key after testing if appropriate.
- Ask the publisher to fix the metadata inconsistency (declare required env vars in the registry) and to document exactly what the orchestrator reads from .env.
功能分析
Type: OpenClaw Skill
Name: swarm-coding-skill
Version: 0.1.2
The skill is highly suspicious due to a critical arbitrary file write vulnerability in `orchestrator.js`. The `parseWorkerOutput` function uses `path.join` with LLM-generated file paths, which can resolve `../` sequences. Combined with the skill's explicit operation on the parent workspace (`WORKSPACE_ROOT = path.resolve(__dirname, '..');`), a malicious prompt could instruct the LLM to write files outside the intended project directory (e.g., `../../../.ssh/authorized_keys`), leading to potential Remote Code Execution (RCE). While the `SKILL.md` and `README.md` warn about writing to the parent workspace, this does not mitigate the underlying path traversal vulnerability.
能力评估
Purpose & Capability
The skill's functionality (orchestrating an LLM to scaffold projects) legitimately requires an OpenRouter API key and filesystem access to write generated projects. However, the registry metadata provided to the platform lists no required env vars while SKILL.md and orchestrator.js both require OPENROUTER_API_KEY — an inconsistency that should be corrected/clarified.
Instruction Scope
SKILL.md and orchestrator.js instruct the agent to read a .env file from the workspace root (parent directory of the skill) and to write project files and persistent logs (swarm-projects/, DECISIONS.md, .learnings/). DECISIONS.md and .learnings/ capture prompts and agent reasoning. Reading the parent .env and persisting detailed logs increases the risk of accidental disclosure of unrelated secrets or sensitive prompt content.
Install Mechanism
There is no external install spec (instruction-only plus a single orchestrator.js). Nothing is downloaded from arbitrary URLs and no installer writes to unexpected system locations. This is the lower-risk install pattern.
Credentials
Requesting OPENROUTER_API_KEY is proportionate to the stated purpose. However, the orchestrator reads the entire .env at the workspace root (not just the declared variable), meaning any other credentials colocated in that .env are accessible by the skill. The registry metadata failing to declare the required env var is another proportionality/consistency issue. The automatic inclusion of Privy/web3 scaffolding when prompts mention blockchain is a functional choice but can lead to generation of auth-related code that requires review.
Persistence & Privilege
The skill persists project files and an ongoing learning log (.learnings/, DECISIONS.md) across runs and records prompts/agent reasoning. While 'always' is false, the retained logs create a persistent record on disk that may contain sensitive inputs. The skill does not modify other skills or system configs, but its local persistence and read access to workspace .env are notable privileges.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install swarm-coding-skill - 安装完成后，直接呼叫该 Skill 的名称或使用
/swarm-coding-skill触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.2
- Introduced a structured YAML frontmatter specifying description, capabilities, required/optional environment variables, output paths, and external services.
- Added explicit warnings about workspace writes and handling of sensitive data in logs and decision files.
- Clarified environment variable requirements and improved documentation on configuration and outputs.
- Enhanced summary of capabilities, including clearer descriptions of knowledge grounding and continuous improvement features.
- Maintained all core functionality and agent workflow as previously described; no file or code changes detected.
v0.1.1
Swarm Coding Skill v0.1.1
- Added _meta.json file for metadata tracking.
- Removed legacy .clawhub/lock.json file.
- Updated documentation in SKILL.md:
- Clarified model usage (switched to `qwen/qwen3-coder` naming).
- Expanded requirements and environment variable details.
- Stressed user responsibility for security, compliance, and deployment.
- Added instructions for optional dry-run mode (`MOCK=1`).
- Improved explanations of where files/logs are written and workspace isolation.
v0.1.0
- Initial release of Swarm Coding Skill: fully autonomous, multi-agent app development from a plain-English prompt.
- Swarm orchestrator analyzes prompts, plans architecture, and manages agent roles for backend, frontend, QA, and DevOps.
- Automated task tracking, dependency management, and conflict avoidance via a generated `swarm.yaml`.
- Quality gates: no merging without passing tests and containerization if needed.
- Output includes a complete project directory, detailed readme, automated tests, container files, decision logs, and learning summaries.
- Continuous improvement support: errors, corrections, and feature requests are logged for smarter future runs.
元数据
常见问题
Hackathon Swarm Coding 是什么？
Autonomously plans, develops, tests, and delivers full software projects from plain-English prompts using coordinated multi-agent roles and automated quality... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 766 次。
如何安装 Hackathon Swarm Coding？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install swarm-coding-skill」即可一键安装，无需额外配置。
Hackathon Swarm Coding 是免费的吗？
是的，Hackathon Swarm Coding 完全免费（开源免费），可自由下载、安装和使用。
Hackathon Swarm Coding 支持哪些平台？
Hackathon Swarm Coding 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Hackathon Swarm Coding？
由 Arun Nadarasa（@arunnadarasa）开发并维护，当前版本 v0.1.2。
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