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api-connector-builder

Build a new API connector or provider by matching the target repo

api-connector-builder とは?

api-connector-builder is a Claude Code agent skill that build a new API connector or provider by matching the target repo.

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

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ドキュメント

API Connector Builder

Use this when the job is to add a repo-native integration surface, not just a generic HTTP client.

The point is to match the host repository's pattern:

  • connector layout
  • config schema
  • auth model
  • error handling
  • test style
  • registration/discovery wiring

When to Use

  • "Build a Jira connector for this project"
  • "Add a Slack provider following the existing pattern"
  • "Create a new integration for this API"
  • "Build a plugin that matches the repo's connector style"

Guardrails

  • do not invent a new integration architecture when the repo already has one
  • do not start from vendor docs alone; start from existing in-repo connectors first
  • do not stop at transport code if the repo expects registry wiring, tests, and docs
  • do not cargo-cult old connectors if the repo has a newer current pattern

Workflow

1. Learn the house style

Inspect at least 2 existing connectors/providers and map:

  • file layout
  • abstraction boundaries
  • config model
  • retry / pagination conventions
  • registry hooks
  • test fixtures and naming

2. Narrow the target integration

Define only the surface the repo actually needs:

  • auth flow
  • key entities
  • core read/write operations
  • pagination and rate limits
  • webhook or polling model

3. Build in repo-native layers

Typical slices:

  • config/schema
  • client/transport
  • mapping layer
  • connector/provider entrypoint
  • registration
  • tests

4. Validate against the source pattern

The new connector should look obvious in the codebase, not imported from a different ecosystem.

Reference Shapes

Provider-style

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

Connector-style

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

TypeScript plugin-style

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

Quality Checklist

  • matches an existing in-repo integration pattern
  • config validation exists
  • auth and error handling are explicit
  • pagination/retry behavior follows repo norms
  • registry/discovery wiring is complete
  • tests mirror the host repo's style
  • docs/examples are updated if expected by the repo

Related Skills

  • 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/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.

api-design

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs. Use when designing or reviewing REST endpoints, resource names, status codes, pagination, or versioning.

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