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Construtor de Conector de API

Constrói um novo conector ou provedor de API correspondendo ao padrão do repositório alvo.

O que é Construtor de Conector de API?

Este skill é projetado para guiar a criação de conectores de API que se integram perfeitamente a um repositório existente, seguindo seu estilo e arquitetura específicos. Em vez de construir um cliente HTTP genérico, o foco é adicionar uma superfície de integração nativa, respeitando a estrutura de diretórios, esquema de configuração, modelo de autenticação, tratamento de erros e estilo de teste do repositório. O skill suporta plataformas que usam conectores ou provedores, como aqueles em Python, TypeScript ou outras linguagens, e é útil para projetos ferramentas para desenvolvedores. O fluxo de trabalho começa aprendendo o estilo da casa através da inspeção de conectores existentes, depois define a integração alvo, constrói em camadas nativas e valida contra o padrão de origem, garantindo que o novo conector pareça óbvio na base de código evitando arquiteturas estrangeiras.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/api-connector-builder
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Documentação

O que Construtor de Conector de API faz?

Use isto quando a tarefa for adicionar uma superfície de integração nativa ao repositório, não apenas um cliente HTTP genérico.

O ponto é corresponder ao padrão do repositório anfitrião:

  • estrutura do conector
  • esquema de configuração
  • modelo de autenticação
  • tratamento de erros
  • estilo de teste
  • fiação de registro/descoberta

Quando Usar

  • "Construir um conector Jira para este projeto"
  • "Adicionar um provedor Slack seguindo o padrão existente"
  • "Criar uma nova integração para esta API"
  • "Construir um plugin que corresponda ao estilo de conector do repositório"

Diretrizes

  • não invente uma nova arquitetura de integração quando o repositório já possui uma
  • não comece apenas pela documentação do fornecedor; comece pelos conectores existentes no repositório primeiro
  • não pare no código de transporte se o repositório espera fiação de registro, testes e documentação
  • não copie cegamente conectores antigos se o repositório tem um padrão mais novo e atual

Fluxo de Trabalho

1. Aprenda o estilo da casa

Inspecione pelo menos 2 conectores/provedores existentes e mapeie:

  • estrutura de arquivos
  • limites de abstração
  • modelo de configuração
  • convenções de repetição / paginação
  • ganchos de registro
  • fixtures de teste e nomenclatura

2. Reduza a integração alvo

Defina apenas a superfície que o repositório realmente precisa:

  • fluxo de autenticação
  • entidades chave
  • operações principais de leitura/escrita
  • paginação e limites de taxa
  • modelo de webhook ou polling

3. Construa em camadas nativas do repositório

Fatias típicas:

  • config/schema
  • cliente/transporte
  • camada de mapeamento
  • ponto de entrada do conector/provedor
  • registro
  • testes

4. Valide contra o padrão de origem

O novo conector deve parecer óbvio na base de código, não importado de um ecossistema diferente.

Formas de Referência

Estilo Provedor

providers/
  provedor_existente/
    __init__.py
    provider.py
    config.py

Estilo Conector

integrations/
  existente/
    client.py
    models.py
    connector.py

Estilo Plugin TypeScript

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

Lista de Verificação de Qualidade

  • corresponde a um padrão de integração existente no repositório
  • validação de configuração existe
  • autenticação e tratamento de erros são explícitos
  • comportamento de paginação/repetição segue as normas do repositório
  • fiação de registro/descoberta está completa
  • testes espelham o estilo do repositório anfitrião
  • documentação/exemplos são atualizados se esperados pelo repositório

Habilidades Relacionadas

  • 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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affaan-m/ECC

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