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SynthClaw

作者 Artur Yakimovich, PhD · GitHub ↗ · v0.1.3 · MIT-0
cross-platform ⚠ suspicious
132
总下载
0
收藏
0
当前安装
4
版本数
在 OpenClaw 中安装
/install synthclaw
功能描述
Render Blender files with agent-controlled procedural parameters for synthetic data generation. Use when generating training data with controlled variations,...
安全使用建议
This skill appears to implement the documented Blender rendering functionality, but there are a few mismatches you should address before installing or running it in a productive environment: - Ensure Blender is installed and available on PATH (SKILL.md and the code require the 'blender' binary, but the registry metadata omitted this). The skill will fail or behave unexpectedly without Blender 4.0+. - The package declares optional metrics dependencies (granatpy, lpips). If you enable compute_metrics, you must install these Python packages and their native dependencies (LPIPS typically requires PyTorch). Installing them increases the runtime footprint — consider doing that in an isolated environment. - The skill will read any file paths you pass (blend_file, output_path directory, reference_image). Be cautious about allowing the agent to pass arbitrary filesystem paths; it can read local images used for metric computation. - Tests reference assets/low.blend and assets/high.blend that are not present in the manifest. Expect some test/setup friction; verify asset availability if you plan to run tests. - Metadata/documentation gaps (missing required binary and missing declared install steps) are likely accidental but important. If you plan to use this skill, verify these items manually, run it in a sandbox/VM first, and avoid exposing sensitive files to the agent's working directory. If you want higher assurance, ask the publisher for an install spec or a small trusted release (e.g., pip package or GitHub release) and a clear list of required system packages for the metrics features before enabling this skill in a production agent.
功能分析
Type: OpenClaw Skill Name: synthclaw Version: 0.1.3 The SynthClaw skill bundle is a legitimate tool designed for automating Blender renders to generate synthetic training data. It demonstrates good security practices by using `subprocess.run(shell=False)` to prevent shell injection, validating that procedural parameters are strictly numeric (int/float), and enforcing timeouts for long-running render processes. The core logic in `src/synthclaw/blender_skill.py` and the Blender-side scripts (`agent_bridge.py`, `analyze_blends.py`) is consistent with the stated purpose and contains no evidence of data exfiltration, unauthorized access, or malicious prompt injection.
能力评估
Purpose & Capability
The code and SKILL.md align with the described purpose: they analyze .blend files, locate Value Nodes, update them and run headless Blender renders. However the registry metadata lists no required binaries while SKILL.md and the code require the 'blender' executable to be on PATH. pyproject.toml declares dependencies (granatpy, lpips) that are relevant for optional metrics but the registry metadata did not surface them. These manifest/metadata omissions are incoherent and worth verifying.
Instruction Scope
Runtime instructions and code are scoped to reading .blend files, optionally a reference image, updating node values, and invoking Blender headlessly. The scripts do not access network endpoints, other credentials, or unrelated system paths. They accept a reference_image path (if provided) and will read that file for metrics — expected for the stated compute_metrics feature.
Install Mechanism
There is no install spec in the registry (instruction-only), but the package includes source files and a pyproject.toml declaring non-trivial dependencies (granatpy, lpips). Because there's no installer step provided, an operator must manually ensure these Python packages (and their native dependencies like PyTorch for LPIPS) are installed. This omission is a configuration/documentation mismatch rather than direct malicious behavior.
Credentials
The skill requests no secrets and does not declare required env vars; the code optionally reads BLENDER_ENGINE and BLENDER_SAMPLES from the environment (benign). The compute_metrics path imports heavy third-party libraries (granatpy, lpips/torch) and will read a reference image file if supplied — both are proportional to the feature but increase the runtime dependency/attack surface. The registry should have declared Blender as a required binary and called out those optional dependencies.
Persistence & Privilege
always:false and no code writes to other skills or system-wide agent settings. The skill runs Blender subprocesses and local scripts but does not claim or request elevated, persistent privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install synthclaw
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /synthclaw 触发
  4. 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
v0.1.3
Corrected license to MIT
v0.1.2
Corrected license to CC-BY-NC 4.0
v0.1.1
Corrected license to CC BY-NC 4.0
v0.1.0
Initial release of synthclaw: agent-controlled procedural rendering in Blender for synthetic data generation. - Enables rendering of Blender files with agent-adjustable procedural Value Node parameters via API. - Supports CYCLES (photorealistic, production) and EEVEE (fast, testing) render engines. - Provides core tools to analyze blend files, render with custom parameters, and return metrics such as LPIPS similarity. - Includes workflow examples and CLI safety features (headless execution, timeout protection, secure parameter handling). - Targeted at users needing automated, parameterized image generation for ML training data.
元数据
Slug synthclaw
版本 0.1.3
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 4
常见问题

SynthClaw 是什么?

Render Blender files with agent-controlled procedural parameters for synthetic data generation. Use when generating training data with controlled variations,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 132 次。

如何安装 SynthClaw?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install synthclaw」即可一键安装,无需额外配置。

SynthClaw 是免费的吗?

是的,SynthClaw 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

SynthClaw 支持哪些平台?

SynthClaw 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 SynthClaw?

由 Artur Yakimovich, PhD(@ayakimovich)开发并维护,当前版本 v0.1.3。

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