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API 連接器建構器

根據目標儲存庫的模式建構新的 API 連接器或提供者。

API 連接器建構器 是什麼？

這項技能會引導 AI 代理程式為現有程式碼庫建構符合其原生模式的 API 連接器或提供者，而非僅是泛用的 HTTP 客戶端。它強調遵循宿主儲存庫的慣例，包括連接器佈局、設定架構、驗證模型、錯誤處理、測試風格及註冊/探索的接線方式。支援的場景涵蓋 Jira 連接器、Slack 提供者等整合任務，並設有保護措施防止發明新架構或盲目複製舊模式。工作流程包含學習既有連接器的風格、鎖定所需的整合範圍、以原生方式分層建構，以及驗證結果與現有程式碼一致。此外提供提供者、連接器與 TypeScript 插件三種常見架構範本，以及品質檢查清單與相關技能連結，適合用於 Cursor 或 Codex 等開發工具中進行一致性的 API 整合開發。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/api-connector-builder
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說明文件

API 連接器建構器 是做什麼的？

當任務是加入一個符合儲存庫原生模式的整合層，而不僅是泛用的 HTTP 客戶端時，請使用此技能。

關鍵在於符合宿主儲存庫的模式：

  • 連接器佈局
  • 設定架構
  • 驗證模型
  • 錯誤處理
  • 測試風格
  • 註冊/探索接線

使用時機

  • "為此專案建構一個 Jira 連接器"
  • "依照現有模式加入一個 Slack 提供者"
  • "為此 API 建立一個新的整合"
  • "建構一個符合儲存庫連接器風格的插件"

保護措施

  • 若儲存庫已有整合架構，不要發明新的架構
  • 不要只從廠商文件開始；應先從儲存庫內既有的連接器著手
  • 若儲存庫預期要加入註冊接線、測試及文件，不要只停在傳輸層程式碼
  • 若儲存庫有更新的現行模式，不要盲目照搬舊的連接器

工作流程

1. 學習內部風格

檢查至少 2 個既有的連接器/提供者，並記錄：

  • 檔案佈局
  • 抽象層邊界
  • 設定模型
  • 重試/分頁慣例
  • 註冊鉤子
  • 測試 fixture 與命名

2. 縮小目標整合範圍

僅定義儲存庫實際需要的表面：

  • 驗證流程
  • 關鍵實體
  • 核心讀取/寫入操作
  • 分頁與速率限制
  • Webhook 或輪詢模型

3. 以儲存庫原生方式分層建構

典型層次：

  • 設定/架構
  • 客戶端/傳輸層
  • 映射層
  • 連接器/提供者入口點
  • 註冊
  • 測試

4. 根據來源模式進行驗證

新的連接器在程式碼庫中應顯得自然，而非像是從不同生態系匯入的。

參考架構

提供者風格

providers/
  existing_provider/
    __init__.py
    provider.py
    config.py

連接器風格

integrations/
  existing/
    client.py
    models.py
    connector.py

TypeScript 插件風格

src/integrations/
  existing/
    index.ts
    client.ts
    types.ts
    test.ts

品質檢查清單

  • 符合儲存庫內既有整合模式
  • 存在設定驗證
  • 驗證與錯誤處理明確
  • 分頁/重試行為遵循儲存庫慣例
  • 註冊/探索接線完整
  • 測試模仿宿主儲存庫風格
  • 若儲存庫預期需更新文件/範例則已更新

相關技能

  • backend-patterns
  • mcp-server-patterns
  • github-ops

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳：このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

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openclaw/openclaw

Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

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obra/superpowers

Obra superpowers agent skills.

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affaan-m/ECC

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...

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affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

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