通过匹配目标仓库现有的集成模式,构建一个新的API连接器或提供者。适用于在不发明第二种架构的情况下添加一个集成。
Scanned 9/4/2026
Install to Claude Code
npx -y skills add affaan-m/everything-claude-code --skill api-connector-builder --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Api Connector Builder?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/affaan-m-api-connector-builder-f10bc23a)More formats (shields.io, HTML) on the badges page.
---
name: api-connector-builder
description: 通过匹配目标仓库现有的集成模式,构建一个新的API连接器或提供者。适用于在不发明第二种架构的情况下添加一个集成。
origin: ECC direct-port adaptation
version: "1.0.0"
---
# API 连接器构建器
当任务需要添加仓库原生的集成接口,而非仅通用 HTTP 客户端时使用此工具。
关键在于匹配宿主仓库的模式:
* 连接器布局
* 配置模式
* 认证模型
* 错误处理
* 测试风格
* 注册/发现机制
## 使用时机
* "为此项目构建 Jira 连接器"
* "按照现有模式添加 Slack 提供商"
* "为此 API 创建新集成"
* "构建符合仓库连接器风格的插件"
## 约束条件
* 若仓库已有集成架构,不得自行发明新架构
* 不得仅从供应商文档入手;应优先参考仓库内现有连接器
* 若仓库需要注册机制、测试和文档,不得仅停留在传输代码层面
* 若仓库有更新的当前模式,不得盲目复制旧连接器
## 工作流程
### 1. 学习内部风格
检查至少 2 个现有连接器/提供商,并映射:
* 文件布局
* 抽象边界
* 配置模型
* 重试/分页约定
* 注册钩子
* 测试夹具和命名规范
### 2. 缩小目标集成范围
仅定义仓库实际需要的接口:
* 认证流程
* 关键实体
* 核心读写操作
* 分页和速率限制
* Webhook 或轮询模型
### 3. 按仓库原生层次构建
典型分层:
* 配置/模式
* 客户端/传输层
* 映射层
* 连接器/提供商入口
* 注册机制
* 测试
### 4. 对照源模式验证
新连接器应在代码库中显得自然,而非从不同生态导入。
## 参考模板
### 提供商风格
```text
providers/
existing_provider/
__init__.py
provider.py
config.py
```
### 连接器风格
```text
integrations/
existing/
client.py
models.py
connector.py
```
### TypeScript 插件风格
```text
src/integrations/
existing/
index.ts
client.ts
types.ts
test.ts
```
## 质量检查清单
* \[ ] 匹配仓库内现有集成模式
* \[ ] 存在配置验证
* \[ ] 认证和错误处理明确
* \[ ] 分页/重试行为遵循仓库规范
* \[ ] 注册/发现机制完整
* \[ ] 测试镜像宿主仓库风格
* \[ ] 若仓库要求,更新文档/示例
## 相关技能
* `backend-patterns`
* `mcp-server-patterns`
* `github-ops`
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.