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Tandemn Tuna Skill
作者
Hetarth Chopra
· GitHub ↗
· v0.0.1
520
总下载
0
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0
当前安装
1
版本数
在 OpenClaw 中安装
/install tandemn-tuna
功能描述
Deploy and serve LLM models on GPU. Compare GPU pricing. Launch vLLM on Modal, RunPod, Cerebrium, Cloud Run, Baseten, or Azure with spot instance fallback. O...
安全使用建议
This skill appears to do what it says (hybrid serverless + spot GPU deployments) but there are important mismatches you should resolve before installing: 1) The skill metadata declares no required environment variables while the instructions require many provider credentials (RunPod API key, gcloud/auth, Azure subscription info, etc.). Assume you will need to provide cloud/API keys. 2) The installer uses a 'uv' tool and an upstream package named tandemn-tuna — verify the 'uv' binary's provenance and inspect the tandemn-tuna package source (the SKILL.md includes a GitHub URL) before running it. 3) Be cautious with the --public option (creates unauthenticated endpoints) and with giving the tool high-privilege cloud credentials — consider using a dedicated, limited-permission cloud account or billing project for testing. If you want this assessed as 'benign' rather than 'suspicious', provide the package repository or package contents (so we can confirm there's no hidden network callbacks or unexpected credential exfiltration) and update the skill metadata to list the exact env vars it needs.
功能分析
Type: OpenClaw Skill
Name: tandemn-tuna
Version: 0.0.1
The skill is classified as suspicious due to its broad capabilities involving extensive cloud resource management and credential handling across multiple providers (AWS, Azure, GCP, Modal, RunPod, Baseten, Cerebrium). The `SKILL.md` instructs the agent to execute various external binaries (`uv`, `aws`, `az`, `gcloud`, `modal`, `truss`, `cerebrium`, and the custom `tuna` CLI) and manage sensitive API keys/credentials. While these actions are necessary for the skill's stated purpose of deploying LLMs, they present a significant attack surface for prompt injection against the agent or potential misuse, leading to unauthorized cloud resource consumption or data exposure if not handled with extreme care by the agent and underlying tools.
能力评估
Purpose & Capability
The name/description (deploy LLMs on serverless + spot GPUs) aligns with the instructions and commands in SKILL.md. Requiring a cloud CLI (aws or az) for spot fallback is plausible. Requiring the 'uv' binary is consistent with the provided 'uv pip install tandemn-tuna' install step, though 'uv' is an uncommon installer and should be verified.
Instruction Scope
The SKILL.md instructs the agent/user to perform provider-specific authentication (RunPod API key, gcloud auth, modal token, Azure subscription/env variables, etc.). However those env vars and credential expectations are not declared in the skill metadata. The instructions also include an option to make endpoints public (--public) which is a meaningful security choice for the operator; the skill does not appear to instruct the agent to read unrelated local files, but it does rely on provider configs and CLIs which will access cloud credentials.
Install Mechanism
Install uses a uv package (tandemn-tuna) that creates a tuna binary. This is an instruction-only skill with an install spec pointing to a package manager rather than a direct download, which is lower risk than arbitrary URL downloads. However, 'uv' is not a mainstream installer in many environments — verify what 'uv' is and that tandemn-tuna's package on the referenced repository/registry is legitimate before installing.
Credentials
Declared required env vars: none. But SKILL.md clearly expects multiple provider credentials and environment variables (RUNPOD_API_KEY, GOOGLE_CLOUD_PROJECT / gcloud auth, Azure subscription/resource group/env, etc.). This mismatch means the skill metadata understates credential needs; users will need to supply sensitive cloud/API credentials for normal operation.
Persistence & Privilege
always:false and no config paths requested. The skill does not demand permanent presence or cross-skill configuration. It will rely on external provider configs (e.g., Modal, gcloud), which is expected for a deployment tool.
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 tandemn-tuna - 安装完成后，直接呼叫该 Skill 的名称或使用
/tandemn-tuna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
v0.0.1
Initial release of tandemn-tuna — deploy and manage LLMs with serverless and spot GPU support.
- Deploy HuggingFace models (Llama, Qwen, Mistral, DeepSeek, Gemma, etc.) to GPUs on Modal, RunPod, Cerebrium, Cloud Run, Baseten, and Azure with optional spot fallback.
- OpenAI-compatible inference endpoint for every deployment.
- Hybrid serverless + spot orchestration for cost savings and zero downtime.
- Built-in commands for GPU price comparison, deployment management, status, and cost dashboard.
- Provider setup guides for quick onboarding across all supported clouds.
元数据
常见问题
Tandemn Tuna Skill 是什么？
Deploy and serve LLM models on GPU. Compare GPU pricing. Launch vLLM on Modal, RunPod, Cerebrium, Cloud Run, Baseten, or Azure with spot instance fallback. O... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 520 次。
如何安装 Tandemn Tuna Skill？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install tandemn-tuna」即可一键安装，无需额外配置。
Tandemn Tuna Skill 是免费的吗？
是的，Tandemn Tuna Skill 完全免费（开源免费），可自由下载、安装和使用。
Tandemn Tuna Skill 支持哪些平台？
Tandemn Tuna Skill 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 Tandemn Tuna Skill？
由 Hetarth Chopra（@choprahetarth）开发并维护，当前版本 v0.0.1。
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