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pinme-apipinme API 文档

Agent Skill

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pinme-api(pinme API 文档)
来源仓库:https://github.com/glitternetwork/pinme
仓库路径:skills/pinme-api
安装命令:
npx skills add https://github.com/glitternetwork/pinme --skill pinme-api
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/glitternetwork/pinme --skill pinme-api

简介

用于辅助 API 设计、接口文档和请求响应结构梳理。

  • 适合生成 OpenAPI 草稿、检查字段命名或整理错误码。
  • 使用时需确认业务语义、鉴权方式和分页规则。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 涉及文档生成时应避免凭空补字段,优先提取现有代码事实。
  • pinme-api 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

PinMe Worker API Integration

Guides how to call PinMe platform's email sending and LLM APIs in a PinMe Worker (TypeScript).

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 — used for send_email and chat/completions authentication
  BASE_URL?: string;    // Optional override for PinMe API base URL, defaults to https://pinme.cloud
}
API_KEY is the sole credential for the Worker to call PinMe platform APIs. When BASE_URL is not set, it defaults to https://pinme.cloud.

API 1: Send Email

Endpoint: POST {BASE_URL}/api/v4/send_email Authentication: X-API-Key header (using env.API_KEY) Sender: Automatically set to {project_name}@pinme.cloud

Request Format

{
  "to": "user@example.com",
  "subject": "Your verification code",
  "html": "<p>Your code is <strong>123456</strong></p>"
}
FieldTypeRequiredDescription
tostringYesRecipient email address
subjectstringYesEmail subject
htmlstringYesHTML body

Response Format

Success (200):

{ "code": 200, "msg": "ok", "data": { "ok": true } }

Errors:

HTTP StatusMeaningdata.error Example
401API Key missing or invalid"X-API-Key header is required" / "Invalid API key"
400Parameter validation failed"Invalid email address" / "Subject is required"
500Email service error"Failed to send email"

Worker Example Code

async function sendEmail(env: Env, to: string, subject: string, html: string): Promise<{ ok: boolean; error?: string }> {
  const baseUrl = env.BASE_URL ?? 'https://pinme.cloud';
  const resp = await fetch(`${baseUrl}/api/v4/send_email`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-API-Key': env.API_KEY,
    },
    body: JSON.stringify({ to, subject, html }),
  });

  const result = await resp.json() as { code: number; msg: string; data?: { ok?: boolean; error?: string } };

  if (resp.status !== 200 || result.code !== 200) {
    return { ok: false, error: result.data?.error || result.msg || 'Unknown error' };
  }
  return { ok: true };
}

// Usage in routes
async function handleSendVerification(request: Request, env: Env): Promise<Response> {
  const { email } = await request.json() as { email: string };
  const code = Math.random().toString().slice(2, 8);

  const result = await sendEmail(env, email, 'Verification Code',
    `<p>Your code is <strong>${code}</strong></p>`);

  if (!result.ok) {
    return json({ error: result.error }, 500);
  }
  return json({ ok: true });
}

API 2: LLM Chat Completions

Endpoint: POST {BASE_URL}/api/v1/chat/completions?project_name={project_name} Authentication: X-API-Key header (using env.API_KEY) Request Body: OpenAI-compatible format, passed through to LLM service as-is Streaming: Supports SSE (stream: true)

Request Format

{
  "model": "openai/gpt-4o-mini",
  "messages": [
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user", "content": "Hello!" }
  ],
  "stream": true
}
project_name is parsed from the Worker's subdomain — see example below. For available models, refer to PinMe LLM Supported Models (OpenAI-compatible format).

Response Format

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 StatusMeaningdata.error Example
401API Key missing or invalid"X-API-Key header is required" / "Invalid API key or project name"
400project_name missing or LLM not configured"project_name is required" / "LLM service not configured for this project"
413Request body exceeds 1MB"Request body too large (max 1MB)"
502LLM service unavailable"LLM service unavailable"

Worker Example Code — Non-streaming

// Get project_name: parsed from the Worker's subdomain
function getProjectName(request: Request): string {
  const host = new URL(request.url).hostname; // e.g. "my-app-1a2b.pinme.pro"
  return host.split('.')[0];
}

async function callLLM(
  env: Env,
  projectName: string,
  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(
    `${baseUrl}/api/v1/chat/completions?project_name=${projectName}`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': env.API_KEY,
      },
      body: JSON.stringify({ model, messages }),
    },
  );

  if (!resp.ok) {
    const err = await resp.json() as { data?: { error?: string } };
    return { content: '', error: err.data?.error || `HTTP ${resp.status}` };
  }

  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 projectName = getProjectName(request);

  const result = await callLLM(env, projectName, [
    { 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 projectName = getProjectName(request);
  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(
    `${baseUrl}/api/v1/chat/completions?project_name=${projectName}`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': env.API_KEY,
      },
      body: JSON.stringify(parsed),
    },
  );

  if (!resp.ok) {
    const err = await resp.json() as { data?: { error?: string } };
    return json({ error: err.data?.error || `HTTP ${resp.status}` }, 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 Patterns

PinMe platform API unified response 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 Unified Error Handler

async function callPinmeAPI<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) {
    try {
      const err = await resp.json() as PinmeResponse;
      return { error: err.data && typeof err.data === 'object' && 'error' in err.data
        ? (err.data as { error: string }).error
        : err.msg || `HTTP ${resp.status}` };
    } catch {
      return { error: `HTTP ${resp.status}` };
    }
  }

  const result = await resp.json() as PinmeResponse<T>;
  if (result.code !== 200) {
    return { error: result.data && typeof result.data === 'object' && 'error' in result.data
      ? (result.data as { error: string }).error
      : result.msg };
  }
  return { data: result.data as T };
}

Usage Examples

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

// Send email
const emailResult = await callPinmeAPI<{ ok: boolean }>(
  `${baseUrl}/api/v4/send_email`, env.API_KEY,
  { to: 'user@example.com', subject: 'Hello', html: '<p>Hi</p>' },
);
if (emailResult.error) return json({ error: emailResult.error }, 500);

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

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02

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03

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

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按下载量换算40

Claude

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按下载量换算29

Cursor

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按下载量换算20

Gemini CLI

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按下载量换算11

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敏感数据

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安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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