
pinme-llm
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/glitternetwork/pinme --skill pinme-llm- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pinme-llm automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
// 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_KEYauthenticates the Worker to PinMe.PROJECT_NAMEis required forchat/completionsand must belong to the same project asAPI_KEY. WhenBASE_URLis not set, usehttps://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.
async function listModels(env: Env): Promise<unknown> {
const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
const resp = await fetch(`${baseUrl}/api/v1/models`, {
headers: { 'X-API-Key': env.API_KEY },
});
if (!resp.ok) {
throw new Error(await extractPinmeOpenRouterError(resp));
}
return await resp.json();
}
Chat Completions API
Endpoint: POST {BASE_URL}/api/v1/chat/completions?project_name={project_name}
Authentication: X-API-Key header (using env.API_KEY)
Request Body: OpenRouter chat/completions format, passed through as-is after a 1MB size check
Streaming: Supports SSE (stream: true)
Web Search: Supports OpenRouter openrouter:web_search server tool via the tools array
Request Format
{
"model": "openai/gpt-4o-mini",
"messages": [
{ "role": "system", "content": "You are a helpful assistant." },
{ "role": "user", "content": "Hello!" }
],
"stream": true
}
Use
env.PROJECT_NAMEfromcreate_worker; always URL-encode it in the query string. For available models, callGET /api/v1/modelsor refer to OpenRouter model IDs.
OpenRouter Web Search
PinMe does not provide a raw search endpoint. To search the web, pass OpenRouter's openrouter:web_search server tool to chat/completions; the model decides whether and when to search.
Always set max_results and max_total_results to keep search volume and cost bounded.
async function searchWithLLM(env: Env, query: string): Promise<string> {
const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
const resp = await fetch(
`${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):
{
"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
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=${encodeURIComponent(env.PROJECT_NAME)}`,
{
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': env.API_KEY,
},
body: JSON.stringify({ model, messages }),
},
);
if (!resp.ok) {
return { content: '', error: await extractPinmeOpenRouterError(resp) };
}
const data = await resp.json() as { choices: Array<{ message: { content: string } }> };
return { content: data.choices[0]?.message?.content || '' };
}
// Usage in routes
async function handleChat(request: Request, env: Env): Promise<Response> {
const { question } = await request.json() as { question: string };
const result = await callLLM(env, [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: question },
]);
if (result.error) {
return json({ error: result.error }, 502);
}
return json({ answer: result.content });
}
Worker Example Code — Streaming (SSE Passthrough)
async function handleChatStream(request: Request, env: Env): Promise<Response> {
const body = await request.text();
const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
// Ensure stream=true in the request
let parsed = JSON.parse(body);
parsed.stream = true;
const resp = await fetch(
`${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
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:
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
async function extractPinmeOpenRouterError(resp: Response): Promise<string> {
const fallback = `HTTP ${resp.status}`;
try {
const body = await resp.clone().json() as PinmeResponse | { error?: { message?: string } } | { error?: string };
if ('data' in body && body.data && typeof body.data === 'object' && 'error' in body.data) {
return String((body.data as { error: unknown }).error);
}
if ('msg' in body && typeof body.msg === 'string' && body.msg) {
return body.msg;
}
if ('error' in body) {
const error = body.error;
if (typeof error === 'string') return error;
if (error && typeof error === 'object' && 'message' in error) {
return String((error as { message: unknown }).message);
}
}
} catch {
try {
const text = await resp.text();
if (text) return text;
} catch {
// Ignore and return fallback below.
}
}
return fallback;
}
Optional JSON Helper
Use this helper for non-streaming POST calls. It returns the raw OpenRouter JSON on success.
async function callOpenRouterJSON<T>(url: string, apiKey: string, body: unknown): Promise<{ data?: T; error?: string }> {
let resp: Response;
try {
resp = await fetch(url, {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'X-API-Key': apiKey },
body: JSON.stringify(body),
});
} catch {
return { error: 'Network error' };
}
if (!resp.ok) {
return { error: await extractPinmeOpenRouterError(resp) };
}
return { data: await resp.json() as T };
}
Usage Example
const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
// Call LLM (non-streaming)
const llmResult = await callOpenRouterJSON<{ choices: Array<{ message: { content: string } }> }>(
`${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);
FAQ
What does the pinme-llm skill do?
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.
How do I install pinme-llm?
Run npx skills add https://github.com/glitternetwork/pinme --skill pinme-llm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pinme-llm/ (personal) or .claude/skills/pinme-llm/ (project).
Which AI agents can use pinme-llm?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pinme-llm safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains pinme-llm?
It is published by glitternetwork in glitternetwork/pinme. It has 499 recorded installs.
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