---
slug: llm-provider-ai
name: openai-ai
version: "1.0.6"
displayName: llm-provider
summary: "管LLM提供商文件/助手/向量库/批次/微调与模型资源"
  and model resources vi...
license: MIT-0
description: |-
  Manage llm-provider files, assistants, vector stores, batches, fine-tuning
  jobs, and model resources vi。Use when 需要AI模型调用、智能对话、Agent编排、LLM应用时使用。不适用于需要100%确定性的关键决策。
tags:
- Creative
tools:
  - - read
- exec
pricing_tier: "L3"
pricing_model: "per_use"
suggested_price: 29.9
---


# llm-provider

Access llm-provider's platform API with API key authentication. Manage files, assistants, vector stores, batches, fine-tuning jobs, and model resources. Generate chat completions, images, audio, and video.

This skill uses [ClawLink](https://claw-link.dev/?utm_source=SkillHub&utm_medium=referral&utm_content=llm-provider-ai) for hosted connection flows and credentials so you do not need to configure llm-provider API access yourself.

### Setup in 3 Steps

| Step 1: Install | Step 2: Pair Account | Step 3: Connect llm-provider |
| --- | --- | --- |
|  |  | *App-specific connection GIF coming soon* |
| Run the install command in Skill平台 | Sign in and approve the device | Open the dashboard and connect llm-provider |

## How It Works

```text
┌─────────────────┐     ┌──────────────┐     ┌──────────────────┐
│   Skill平台      │────▶│   ClawLink   │────▶│   llm-provider API     │
│   (User Chat)   │     │   (API Key)  │     │                  │
└─────────────────┘     └──────────────┘     └──────────────────┘
          │                       │                       │
          │  1. Install Plugin   │                       │
          │  2. Pair Device      │                       │
          │3. Connect llm-provider   │                       │
          │                      │  4. Secure Proxy      │
          │                      │  5. API Requests      │
          │                      │                       │
          ▼                      ▼                       ▼
    ┌──────────┐           ┌──────────┐           ┌──────────┐
    │  SKILL   │           │ Dashboard│           │  llm-provider  │
    │  File    │           │ Auth     │           │ Platform │
    └──────────┘           └──────────┘           └──────────┘
```

## Install

Install the verified ClawLink plugin from SkillHub. After the user confirms, complete the full setup so ClawLink tools are visible in the next Skill平台 chat.

```bash
skill-platform plugins install SkillHub:clawlink-plugin
skill-platform config set tools.alsoAllow '["clawlink-plugin"]' --strict-json
skill-platform gateway restart
```

Then tell the user: "Skill平台 has been restarted. Send `/new` as a standalone message to start a fresh chat, then ask for llm-provider again."

## Quick Start

```bash
clawlink_call_tool --tool "openai_list_assistants" --params '{"limit": 10}'

clawlink_call_tool --tool "openai_create_chat_completion" --params '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}'

clawlink_call_tool --tool "openai_list_files" --params '{"purpose": "batch"}'
```

## Authentication

All llm-provider tool calls are authenticated automatically by ClawLink using the user's llm-provider API key.

**No API key is required in chat.** ClawLink stores the API key securely and injects it into every llm-provider API request on the user's behalf.

### Getting Connected

1. Install the ClawLink plugin (see Install above).
2. Pair the plugin with `clawlink_begin_pairing` if it is not configured yet.
3. Open <https://claw-link.dev/dashboard?add=llm-provider> and connect llm-provider.
4. Call `clawlink_list_integrations` to verify the connection is active.

## Connection Management

### List Connections

```bash
clawlink_list_integrations
```

**Response:** Returns all connected integrations. Look for `llm-provider` in the list.

### Verify Connection

```bash
clawlink_list_tools --integration llm-provider
```

**Response:** Returns the live tool catalog for llm-provider.

### Reconnect

If llm-provider tools are missing or the connection shows an error:

1. Direct the user to <https://claw-link.dev/dashboard?add=llm-provider>
2. After they confirm, call `clawlink_list_integrations` to verify
3. Then call `clawlink_list_tools --integration llm-provider`

## Security& Permissions

* Access is scoped to resources within the connected llm-provider organization and account.
* **All write operations require explicit user confirmation.** Before executing any create, update, or delete call, confirm the target resource and intended effect with the user.
* Destructive actions (delete file, delete assistant, cancel batch) must be confirmed.
* Fine-tuning, batch jobs, and video generation are asynchronous — poll for completion.

## Tool Reference

### Assistants & Threads

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_list_assistants` | List assistants to discover by name or metadata | Read |
| `openai_create_assistant` | Create a new assistant with model and tools | Write |
| `openai_modify_assistant` | Update an existing assistant | Write |
| `openai_delete_assistant` | Delete an assistant permanently | Write |
| `openai_create_thread` | Create a new conversation thread | Write |
| `openai_create_message` | Add a message to a thread | Write |
| `openai_create_run` | Execute an assistant on a thread | Write |
| `openai_list_messages` | List messages in a thread | Read |
| `openai_list_runs` | List runs for a thread | Read |
| `openai_cancel_run` | Cancel an in-progress run | Write |
| `openai_list_run_steps` | List steps in a run | Read |
| `openai_get_run_step` | Get details of a specific run step | Read |

### Files & Vector Stores

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_list_files` | List uploaded files | Read |
| `openai_upload_file` | Upload a file to llm-provider | Write |
| `openai_delete_file` | Delete an uploaded file | Write |
| `openai_download_file` | Download file content | Read |
| `openai_create_vector_store` | Create a vector store | Write |
| `openai_list_vector_stores` | List vector stores | Read |
| `openai_get_vector_store` | Get vector store details | Read |
| `openai_delete_vector_store` | Delete a vector store | Write |
| `openai_create_vector_store_file` | Add a file to a vector store | Write |
| `openai_list_vector_store_files` | List files in a vector store | Read |
| `openai_delete_vector_store_file` | Remove a file from a vector store | Write |

### Batches & Fine-Tuning

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_list_batches` | List batch jobs | Read |
| `openai_create_batch` | Create a batch from a JSONL file | Write |
| `openai_cancel_batch` | Cancel an in-progress batch | Write |
| `openai_list_fine_tunes` | List fine-tuning jobs | Read |
| `openai_create_fine_tuning_job` | Start a fine-tuning job | Write |
| `openai_list_fine_tuning_events` | Get events for a fine-tuning job | Read |
| `openai_list_fine_tuning_job_checkpoints` | List checkpoints from a fine-tuning job | Read |

### Generations & Completions

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_create_chat_completion` | Create a chat completion | Read |
| `openai_create_completion` | Create a text completion (legacy) | Read |
| `openai_create_response` | Create a response via Responses API | Write |
| `openai_create_embeddings` | Generate text embeddings | Read |
| `openai_create_image` | Generate an image | Write |
| `openai_create_image_edit` | Edit an existing image | Write |
| `openai_create_image_variation` | Create an image variation | Write |
| `openai_create_speech` | Generate text-to-speech audio | Read |
| `openai_create_audio_transcription` | Transcribe audio to text | Read |
| `openai_create_audio_translation` | Translate audio to English | Read |
| `openai_create_video` | Generate a video | Write |
| `openai_list_videos` | List video generation jobs | Read |
| `openai_get_video` | Get video generation status | Read |
| `openai_download_video` | Download video content | Read |
| `openai_create_moderation` | Classify content for policy violations | Read |

### Evaluations

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_list_evals` | List evaluations | Read |
| `openai_get_eval` | Get evaluation details | Read |
| `openai_create_eval` | Create an evaluation | Write |
| `openai_delete_eval` | Delete an evaluation | Write |
| `openai_create_eval_run` | Start an evaluation run | Write |
| `openai_get_eval_run` | Get evaluation run status | Read |
| `openai_list_eval_runs` | List evaluation runs | Read |
| `openai_cancel_eval_run` | Cancel an evaluation run | Write |

### Other Resources

| Tool | Description | Mode |
| --- | --- | --- |
| `openai_list_models` | List available models | Read |
| `openai_list_engines` | List available engines | Read |
| `openai_get_input_token_counts` | Calculate token counts for a request | Read |
| `openai_create_skill` | Create a skill from files | Write |
| `openai_list_skills` | List skills | Read |
| `openai_delete_skill` | Delete a skill | Write |
| `openai_create_container` | Create an isolated execution container | Write |
| `openai_list_containers` | List containers | Read |
| `openai_delete_container` | Delete a container | Write |

## 示例

### Create a chat completion

```bash
clawlink_call_tool --tool "openai_create_chat_completion" \
  --params '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "max_tokens": 500
  }'
```

### Create an assistant

```bash
clawlink_call_tool --tool "openai_create_assistant" \
  --params '{
    "name": "Code Reviewer",
    "model": "gpt-4o",
    "instructions": "You review code and suggest improvements.",
    "tools": [{"type": "code_interpreter"}]
  }'
```

### Upload a file and create a vector store

```bash
clawlink_call_tool --tool "openai_upload_file" \
  --params '{
    "file": "@./knowledge.pdf",
    "purpose": "assistants"
  }'

clawlink_call_tool --tool "openai_create_vector_store_file" \
  --params '{
    "vector_store_id": "VS_ID",
    "file_id": "FILE_ID"
  }'
```

### Create a batch job

```bash
clawlink_call_tool --tool "openai_create_batch" \
  --params '{
    "input_file_id": "FILE_ID",
    "endpoint": "chat/completions",
    "model": "gpt-4o-mini",
    "completion_window": "24h"
  }'
```

### Generate an image

```bash
clawlink_call_tool --tool "openai_create_image" \
  --params '{
    "model": "dall-e-3",
    "prompt": "A minimalist diagram showing the Skill平台 to ClawLink to Provider flow",
    "size": "1024x1024",
    "n": 1
  }'
```

## Discovery Workflow

1. Call `clawlink_list_integrations` to confirm llm-provider is connected.
2. Call `clawlink_list_tools --integration llm-provider` to see the live catalog.
3. Treat the returned list as the source of truth. Do not guess or assume what tools exist.
4. If the user describes a capability but the exact tool is unclear, call `clawlink_search_tools` with a short query and integration `llm-provider`.
5. If no llm-provider tools appear, direct the user to <https://claw-link.dev/dashboard?add=llm-provider>.

## Execution Workflow

```text
┌─────────────────────────────────────────────────────────────┐
│  READ OPERATIONS (Safe)                                     │
│  list → get → search → describe → call                      │
│                                                             │
│  Example: List models → Create completion → Return result   │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│  WRITE OPERATIONS (Require Confirmation)                     │
│  list → get → describe → preview → confirm → call           │
│                                                             │
│  Example: Describe tool → Preview changes → User approves   │
│           → Execute update                                  │
└─────────────────────────────────────────────────────────────┘
```

1. For unfamiliar tools, ambiguous requests, or any write action, call `clawlink_describe_tool` first.
2. Use the returned guidance, schema, `whenToUse`, `askBefore`, `safeDefaults`, `examples`, and `followups` to shape the call.
3. Prefer read, list, search, and get operations before writes when that reduces ambiguity.
4. For writes or anything marked as requiring confirmation, call `clawlink_preview_tool` first.
5. Execute with `clawlink_call_tool`. Pass confirmation only after the preview matches the user's intent.
6. If the tool call fails, report the real error. Do not invent results or restate the failure as a missing capability unless the live catalog supports that conclusion.

## Notes

* Use model IDs exactly as returned by `openai_list_models` — misspelled or unsupported IDs cause validation errors.
* Asynchronous operations (batch, fine-tune, video) return a job ID — poll `get` endpoints to check completion.
* Thread IDs must be persisted after creation and passed to all subsequent calls.
* Files uploaded for batch processing must have `purpose: "batch"`; files for assistants must have `purpose: "assistants"`.
* Video generation is asynchronous — poll `openai_get_video` until status is `completed`.

## Error Handling

| Status / Error | Meaning |
| --- | --- |
| Tool not found | The tool name does not exist in the current catalog. Verify with `clawlink_list_tools --integration llm-provider`. |
| Missing connection | llm-provider is not connected. Direct the user to <https://claw-link.dev/dashboard?add=llm-provider>. |
| `invalid_request_error` | Invalid parameter or model not available. Verify model ID with `openai_list_models`. |
| `RateLimitError` | Too many requests. Wait and retry with exponential backoff. |
| `authentication_error` | Invalid API key. Reconnect llm-provider at the dashboard. |
| Write rejected | User did not confirm a write action. Always confirm before executing writes. |

### 错误处理

1. Check that the ClawLink plugin is installed:

   bash

   ```
   skill-platform plugins list
   ```
2. If the plugin is installed but tools are missing, tell the user to send `/new` as a standalone message to reload the catalog.
3. If a fresh chat does not help, run:

   bash

   ```
   skill-platform config set tools.alsoAllow '["clawlink-plugin"]' --strict-json
   skill-platform gateway restart
   ```
4. After restart, tell the user to send `/new` again and retry.

### Troubleshooting: Invalid Tool Call

1. Ensure the integration slug is exactly `llm-provider`.
2. Use `clawlink_describe_tool` to verify parameter names and types before calling.
3. For write operations, always call `clawlink_preview_tool` first.

## Resources

* [llm-provider API Reference](https://platform.llm-provider.com/docs/api-reference)
* [Assistants API Overview](https://platform.llm-provider.com/docs/assistants/overview)
* [Fine-tuning Documentation](https://platform.llm-provider.com/docs/guides/fine-tuning)
* [Batch API](https://platform.llm-provider.com/docs/guides/batch)
* ClawLink: <https://claw-link.dev/?utm_source=SkillHub&utm_medium=referral&utm_content=llm-provider-ai>
* ClawLink Docs: <https://docs.claw-link.dev/skill-platform>
* ClawLink Verification: <https://claw-link.dev/verify>

## Related Skills

* [llm-provider](https://SkillHub.ai/hith3sh/llm-provider-ai) — For this skill's native documentation

---

**Powered by [ClawLink](https://claw-link.dev/?utm_source=SkillHub&utm_medium=referral&utm_content=llm-provider-ai)** — an integration hub for Skill平台

## 依赖说明

### 运行环境
- **Agent平台**: 支持SKILL.md的任意AI Agent(ai-assistant Code / Cursor / Codex / Gemini CLI等)
- **操作系统**: Windows / macOS / Linux

### 依赖说明
| 依赖项 | 类型 | 是否必需 | 获取方式 |
|:-------|:-----|:---------|:---------|
| LLM API | API | 必需 | 由Agent内置LLM提供 |

### API Key 配置
- 本Skill基于Markdown指令,无需额外API Key(除内容中明确标注的外部API)

### 可用性分类
- **分类**: MD+EXEC(纯Markdown指令,部分功能需要exec命令行执行能力)
- **说明**: 基于Markdown的AI Skill,通过自然语言指令驱动Agent执行任务

## 核心能力

- Manage llm-provider files, assistants, vector stores, batches, fine-tuning
  jobs, and model resources vi
- 触发关键词: files, manage, vector, assistants, llm-provider

## 适用场景

| 场景 | 输入 | 输出 |
|------|------|------|
| 基础使用 | 用户请求 | 处理结果 |

**不适用于**：需要人工判断的复杂决策场景

## 常见问题

### Q1: 如何开始使用OpenAI？
A: 请先阅读使用流程章节，确认环境满足依赖说明中的要求。

### Q2: 遇到错误怎么办？
A: 请参考错误处理章节，按照表格中的处理方式操作。

### Q3: OpenAI有什么限制？
A: 请参考已知限制章节了解具体限制。

## 已知限制

- 需要API Key，无Key环境无法使用
- 性能取决于底层模型能力