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pinme-llm — Claude Code skill for OpenRouter in Workers

Tells the AI how to call OpenRouter models from a PinMe TypeScript Worker through the platform proxy: chat, streaming, web search.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add glitternetwork/pinme --skill pinme-llm -a claude-code
Open on GitHub

What pinme-llm does

The skill fires when a PinMe Worker written in TypeScript has to reach an LLM. It hands the agent working code for three endpoints: GET /api/v1/models to list models, POST /api/v1/chat/completions which forwards an OpenRouter-shaped body, and web search via the openrouter:web_search server tool.

Your real OpenRouter key never lands in the Worker. PinMe injects API_KEY, PROJECT_NAME and an optional BASE_URL at creation time. Calls carry the X-API-Key header, and the project_name query value must belong to the same project as the key — a mismatch comes back as 401.

The skill documents the proxy's error codes: 400 when project_name is missing or no OpenRouter key is configured, 403 on low balance, 413 past 1MB of body, 502 when the service is down. OpenRouter replies pass through untouched, so parse them in OpenRouter's own shape. Set max_results and max_total_results on web search, otherwise nothing caps how much gets searched or billed.

What it helps with

  • call an OpenRouter model from a TypeScript Worker without holding the real key
  • stream a model reply over SSE from a request handler
  • attach web search to a chat call with openrouter:web_search
  • map PinMe proxy errors to sensible responses for the client
  • fetch the list of available models straight from the Worker

Example prompts for the agent

  • Add a chat/completions call to backend/src/worker.ts using openai/gpt-4o-mini and read the key from env.API_KEY
  • Stream the model output as SSE from my Worker handler
  • Wire web search into the request with openrouter:web_search and cap the result count

How to install pinme-llm

1Skills CLI (Vercel)

npx skills add glitternetwork/pinme --skill pinme-llm -a claude-code

2Manually: put the files in place

~/.claude/skills/pinme-llm/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/pinme-llm/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about pinme-llm

How do I install pinme-llm in Claude Code?

Run npx skills add glitternetwork/pinme --skill pinme-llm -a claude-code. The skill file lands at ~/.claude/skills/pinme-llm/SKILL.md.

What does pinme-llm do?

It steers the agent toward correct TypeScript Worker code for OpenRouter calls through the PinMe proxy: model listing, chat, streaming and web search.

Do I need an OpenRouter key in the project?

No. The Worker authenticates with the PinMe project key in env.API_KEY, and PROJECT_NAME has to point at that same project. The OpenRouter key stays on the platform side.

Source text

SKILL.md · MIT
---
name: pinme-llm
description: Use this skill when a PinMe project (Worker TypeScript) needs to call OpenRouter-backed LLM APIs, including models, chat/completions, streaming, or OpenRouter web search. Guides AI to generate correct Worker TS code.
---

# PinMe Worker OpenRouter API Integration

Guides how to call PinMe platform's OpenRouter proxy APIs in a PinMe Worker (TypeScript). Workers use the PinMe project API key; they never hold the real OpenRouter API key.

## Environment Variables

The following environment variables are automatically injected when the Worker is created — no manual configuration needed:

```typescript
// backend/src/worker.ts
export interface Env {
  DB: D1Database;
  API_KEY: string;       // Project API Key from create_worker
  PROJECT_NAME: string;  // Actual project_name from create_worker; must match API_KEY
  BASE_URL?: string;     // Optional override for PinMe API base URL, defaults to https://pinme.cloud
}
```

> `API_KEY` authenticates the Worker to PinMe. `PROJECT_NAME` is required for `chat/completions` and must belong to the same project as `API_KEY`. When `BASE_URL` is not set, use `https://pinme.cloud`.

---

## Models API

**Endpoint:** `GET {BASE_URL}/api/v1/models`
**Authentication:** `X-API-Key` header (using `env.API_KEY`)
**Request Body:** none

Use this when the Worker needs to list available OpenRouter models. The response body, status, and headers are passed through from OpenRouter `/models`.

```typescript
async function listModels(env: Env): Promise<unknown> {
  const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
  const resp = await fetch(`${baseUrl}/api/v1/models`, {
    headers: { 'X-API-Key': env.API_KEY },
  });

  if (!resp.ok) {
    throw new Error(await extractPinmeOpenRouterError(resp));
  }

  return await resp.json();
}
```

---

## Chat Completions API

**Endpoint:** `POST {BASE_URL}/api/v1/chat/completions?project_name={project_name}`
**Authentication:** `X-API-Key` header (using `env.API_KEY`)
**Request Body:** OpenRouter chat/completions format, passed through as-is after a 1MB size check
**Streaming:** Supports SSE (`stream: true`)
**Web Search:** Supports OpenRouter `openrouter:web_search` server tool via the `tools` array

### Request Format

```json
{
  "model": "openai/gpt-4o-mini",
  "messages": [
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user", "content": "Hello!" }
  ],
  "stream": true
}
```

> Use `env.PROJECT_NAME` from `create_worker`; always URL-encode it in the query string. For available models, call `GET /api/v1/models` or refer to OpenRouter model IDs.

### OpenRouter Web Search

PinMe does not provide a raw search endpoint. To search the web, pass OpenRouter's `openrouter:web_search` server tool to `chat/completions`; the model decides whether and when to search.

Always set `max_results` and `max_total_results` to keep search volume and cost bounded.

```typescript
async function searchWithLLM(env: Env, query: string): Promise<string> {
  const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
  const resp = await fetch(
    `${baseUrl}/api/v1/chat/completions?project_name=${encodeURIComponent(env.PROJECT_NAME)}`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': env.API_KEY,
      },
      body: JSON.stringify({
        model: 'openai/gpt-5.2',
        messages: [{ role: 'user', content: query }],
        tools: [
          {
            type: 'openrouter:web_search',
            parameters: {
              engine: 'auto',
              max_results: 5,
              max_total_results: 10,
            },
          },
        ],
      }),
    },
  );

  if (!resp.ok) {
    throw new Error(await extractPinmeOpenRouterError(resp));
  }

  const data = await resp.json() as { choices: Array<{ message?: { content?: string } }> };
  return data.choices[0]?.message?.content ?? '';
}
```

### Response Format

Successful requests return OpenRouter's raw response body.

**Non-streaming Success (200):**
```json
{
  "id": "chatcmpl-...",
  "choices": [{ "message": { "role": "assistant", "content": "Hello!" }, "finish_reason": "stop" }],
  "usage": { "prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15 }
}
```

**Streaming Success (200):** SSE format
```
data: {"choices":[{"delta":{"content":"Hello"}}]}
data: {"choices":[{"delta":{"content":" there"}}]}
data: [DONE]
```

**Errors:**

| HTTP Status | Meaning | data.error Example |
|-------------|---------|-------------------|
| 401 | API Key missing, invalid, or mismatched with project_name | `"X-API-Key header is required"` / `"Invalid API key"` / `"Invalid API key or project name"` |
| 400 | project_name missing or OpenRouter key not configured | `"project_name is required"` / `"LLM service not configured for this project"` |
| 403 | LLM balance insufficient or disabled | `"Insufficient balance, please recharge to continue using LLM service"` |
| 413 | Request body exceeds 1MB | `"Request body too large (max 1MB)"` |
| 500 | Proxy failed before upstream request | `"Failed to build request"` |
| 502 | LLM service unavailable | `"LLM service unavailable"` |

If OpenRouter receives the request and returns a 4xx/5xx, PinMe passes through OpenRouter's status, headers, and response body instead of wrapping it.

### Worker Example Code — Non-streaming

```typescript
async function callLLM(
  env: Env,
  messages: Array<{ role: string; content: string }>,
  model = 'openai/gpt-4o-mini',
): Promise<{ content: string; error?: string }> {
  const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
  const resp = await fetch(
    `${baseUrl}/api/v1/chat/completions?project_name=${encodeURIComponent(env.PROJECT_NAME)}`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': env.API_KEY,
      },
      body: JSON.stringify({ model, messages }),
    },
  );

  if (!resp.ok) {
    return { content: '', error: await extractPinmeOpenRouterError(resp) };
  }

  const data = await resp.json() as { choices: Array<{ message: { content: string } }> };
  return { content: data.choices[0]?.message?.content || '' };
}

// Usage in routes
async function handleChat(request: Request, env: Env): Promise<Response> {
  const { question } = await request.json() as { question: string };

  const result = await callLLM(env, [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: question },
  ]);

  if (result.error) {
    return json({ error: result.error }, 502);
  }
  return json({ answer: result.content });
}
```

### Worker Example Code — Streaming (SSE Passthrough)

```typescript
async function handleChatStream(request: Request, env: Env): Promise<Response> {
  const body = await request.text();
  const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';

  // Ensure stream=true in the request
  let parsed = JSON.parse(body);
  parsed.stream = true;

  const resp = await fetch(
    `${baseUrl}/api/v1/chat/completions?project_name=${encodeURIComponent(env.PROJECT_NAME)}`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': env.API_KEY,
      },
      body: JSON.stringify(parsed),
    },
  );

  if (!resp.ok) {
    return json({ error: await extractPinmeOpenRouterError(resp) }, resp.status);
  }

  // Pass through SSE stream directly
  return new Response(resp.body, {
    status: 200,
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive',
      ...CORS_HEADERS,
    },
  });
}
```

### Frontend SSE Stream Consumer Example

```typescript
async function streamChat(question: string, onChunk: (text: string) => void): Promise<void> {
  const resp = await fetch(getApiUrl('/api/chat/stream'), {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ question }),
  });

  const reader = resp.body!.getReader();
  const decoder = new TextDecoder();
  let buffer = '';

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    buffer += decoder.decode(value, { stream: true });
    const lines = buffer.split('\n');
    buffer = lines.pop()!; // Keep incomplete line

    for (const line of lines) {
      if (!line.startsWith('data: ')) continue;
      const payload = line.slice(6);
      if (payload === '[DONE]') return;

      const chunk = JSON.parse(payload) as { choices: Array<{ delta: { content?: string } }> };
      const content = chunk.choices[0]?.delta?.content;
      if (content) onChunk(content);
    }
  }
}
```

---

## Error Handling Pattern

For `/api/v1/models` and `/api/v1/chat/completions`, successful responses are raw OpenRouter responses. Proxy failures before the OpenRouter request use PinMe's wrapped error format:

```typescript
interface PinmeResponse<T = unknown> {
  code: number;   // 200=success, other=failure
  msg: string;    // "ok" | "error" | "invalid params"
  data?: T;       // Business data on success, may contain { error: string } on failure
}
```

### Recommended Error Extractor

```typescript
async function extractPinmeOpenRouterError(resp: Response): Promise<string> {
  const fallback = `HTTP ${resp.status}`;
  try {
    const body = await resp.clone().json() as PinmeResponse | { error?: { message?: string } } | { error?: string };
    if ('data' in body && body.data && typeof body.data === 'object' && 'error' in body.data) {
      return String((body.data as { error: unknown }).error);
    }
    if ('msg' in body && typeof body.msg === 'string' && body.msg) {
      return body.msg;
    }
    if ('error' in body) {
      const error = body.error;
      if (typeof error === 'string') return error;
      if (error && typeof error === 'object' && 'message' in error) {
        return String((error as { message: unknown }).message);
      }
    }
  } catch {
    try {
      const text = await resp.text();
      if (text) return text;
    } catch {
      // Ignore and return fallback below.
    }
  }
  return fallback;
}
```

### Optional JSON Helper

Use this helper for non-streaming `POST` calls. It returns the raw OpenRouter JSON on success.

```typescript
async function callOpenRouterJSON<T>(url: string, apiKey: string, body: unknown): Promise<{ data?: T; error?: string }> {
  let resp: Response;
  try {
    resp = await fetch(url, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json', 'X-API-Key': apiKey },
      body: JSON.stringify(body),
    });
  } catch {
    return { error: 'Network error' };
  }

  if (!resp.ok) {
    return { error: await extractPinmeOpenRouterError(resp) };
  }

  return { data: await resp.json() as T };
}
```

### Usage Example

```typescript
const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';

// Call LLM (non-streaming)
const llmResult = await callOpenRouterJSON<{ choices: Array<{ message: { content: string } }> }>(
  `${baseUrl}/api/v1/chat/completions?project_name=${encodeURIComponent(env.PROJECT_NAME)}`, env.API_KEY,
  { model: 'openai/gpt-4o-mini', messages: [{ role: 'user', content: 'Hi' }] },
);
if (llmResult.error) return json({ error: llmResult.error }, 502);
```

© glitternetwork · MIT · glitternetwork/pinme

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