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api-connector-builder

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

api-connector-builder 是什么?

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

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/api-connector-builder

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文档

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

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affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

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.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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