API Integration: Building system connector...

Develops system-to-system connectors with REST/GraphQL patterns, authentication flows, rate limit handling, data mapping, error recovery, and SDK wrapper generation.

Activation

When this skill activates, output:

API Integration: Building system connector...

Then execute the protocol below.

Context Guard

Common Mistakes

Protocol

Step 1: Gather Integration Requirements

If the user hasn't provided details, ask:

1. Source system: where does the data come from? (API name, docs URL)
2. Destination: where does it go? (your DB, another API, file)
3. Data: what entities are synced? (users, orders, products, events)
4. Direction: one-way, two-way, or event-driven?
5. Auth: how does the API authenticate? (API key, OAuth 2.0, JWT, Basic)
6. Volume: how much data? How often? (1K records/day vs 1M)

Step 2: Implement Authentication

OAuth 2.0 token refresh pattern:

class ApiClient {
  private accessToken: string;
  private refreshToken: string;
  private expiresAt: number;

  async request(method: string, path: string, body?: any): Promise<any> {
    if (Date.now() >= this.expiresAt - 60_000) { // Refresh 60s early
      await this.refreshAccessToken();
    }

    const response = await fetch(`${this.baseUrl}${path}`, {
      method,
      headers: {
        'Authorization': `Bearer ${this.accessToken}`,
        'Content-Type': 'application/json',
      },
      body: body ? JSON.stringify(body) : undefined,
    });

    if (response.status === 401) {
      await this.refreshAccessToken();
      return this.request(method, path, body); // Retry once
    }

    return response.json();
  }
}

Step 3: Handle Rate Limiting

Rate limit detection and backoff:

async function requestWithRateLimit(
  fn: () => Promise<Response>,
  maxRetries = 3
): Promise<Response> {
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    const response = await fn();

    if (response.status === 429) {
      const retryAfter = parseInt(response.headers.get('Retry-After') || '0');
      const waitMs = retryAfter > 0
        ? retryAfter * 1000
        : Math.min(1000 * Math.pow(2, attempt), 30_000); // Exponential backoff, max 30s

      console.log(`Rate limited. Waiting ${waitMs}ms (attempt ${attempt + 1})`);
      await sleep(waitMs);
      continue;
    }

    return response;
  }
  throw new Error('Rate limit exceeded after max retries');
}

Proactive rate limiting (token bucket):

class RateLimiter {
  private tokens: number;
  private lastRefill: number;

  constructor(
    private maxTokens: number,    // e.g., 100
    private refillRate: number,   // tokens per second, e.g., 10
  ) {
    this.tokens = maxTokens;
    this.lastRefill = Date.now();
  }

  async acquire(): Promise<void> {
    this.refill();
    if (this.tokens <= 0) {
      const waitMs = (1 / this.refillRate) * 1000;
      await sleep(waitMs);
      this.refill();
    }
    this.tokens--;
  }

  private refill(): void {
    const now = Date.now();
    const elapsed = (now - this.lastRefill) / 1000;
    this.tokens = Math.min(this.maxTokens, this.tokens + elapsed * this.refillRate);
    this.lastRefill = now;
  }
}

Step 4: Build Data Mapper

Mapping layer pattern:

// Define field mappings declaratively
interface FieldMapping {
  source: string;        // Source field path (dot notation)
  target: string;        // Target field path
  transform?: (val: any) => any;  // Optional transformation
  required?: boolean;
}

const orderMappings: FieldMapping[] = [
  { source: 'id',              target: 'externalId',  required: true },
  { source: 'customer.email',  target: 'email',       required: true },
  { source: 'total_price',     target: 'amount',      transform: (v) => parseFloat(v) * 100 }, // dollars to cents
  { source: 'created_at',      target: 'createdAt',   transform: (v) => new Date(v).toISOString() },
  { source: 'line_items',      target: 'items',       transform: mapLineItems },
];

function mapRecord(source: any, mappings: FieldMapping[]): any {
  const target: any = {};
  for (const m of mappings) {
    const value = getNestedValue(source, m.source);
    if (value === undefined && m.required) {
      throw new Error(`Missing required field: ${m.source}`);
    }
    if (value !== undefined) {
      setNestedValue(target, m.target, m.transform ? m.transform(value) : value);
    }
  }
  return target;
}

Step 5: Handle Pagination

Common pagination patterns:

Generic paginator:

async function* paginate<T>(
  fetchPage: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator<T> {
  let cursor: string | undefined;
  do {
    const page = await fetchPage(cursor);
    for (const item of page.data) {
      yield item;
    }
    cursor = page.nextCursor;
  } while (cursor);
}

// Usage
for await (const order of paginate(fetchOrders)) {
  await processOrder(order);
}

Step 6: Error Recovery & Sync State

Sync state tracking:

CREATE TABLE sync_state (
  integration  VARCHAR(100) PRIMARY KEY,
  last_cursor  VARCHAR(255),
  last_sync_at TIMESTAMPTZ  NOT NULL,
  items_synced INTEGER      NOT NULL DEFAULT 0,
  status       VARCHAR(20)  NOT NULL DEFAULT 'idle', -- idle | running | failed
  error        TEXT
);

Incremental sync pattern:

async function incrementalSync(integration: string): Promise<void> {
  const state = await getSyncState(integration);
  let cursor = state.lastCursor;
  let count = 0;

  try {
    await updateSyncState(integration, { status: 'running' });

    for await (const item of paginate((c) => fetchItems(c || cursor))) {
      await processItem(item);
      cursor = item.id; // Track progress
      count++;

      // Checkpoint every 100 items (resume from here on failure)
      if (count % 100 === 0) {
        await updateSyncState(integration, { lastCursor: cursor, itemsSynced: count });
      }
    }

    await updateSyncState(integration, {
      status: 'idle', lastCursor: cursor,
      lastSyncAt: new Date(), itemsSynced: count
    });
  } catch (error) {
    await updateSyncState(integration, {
      status: 'failed', lastCursor: cursor, error: error.message
    });
    throw error;
  }
}

Step 7: Production Checklist

• Authentication handles token refresh (no manual token replacement)
• Rate limiter respects API's documented limits
• Retry logic with exponential backoff for 429 and 5xx responses
• Pagination handles all pages (not just page 1)
• Data mapper validates required fields before writing
• Sync state persisted, can resume from last checkpoint on failure
• Structured logging with correlation IDs
• Secrets in environment variables or secrets manager
• Error alerts configured (Slack, email, or PagerDuty)
• Integration tested with production-volume data

Output Format

# API Integration: [Source] → [Destination]

## Overview
- **Direction:** [One-way / Two-way / Event-driven]
- **Entities:** [What's being synced]
- **Auth:** [Auth method]
- **Rate limit:** [X requests/second]
- **Sync frequency:** [Real-time / Every X minutes / Daily]

## Authentication
[Implementation from Step 2]

## Rate Limiting
[Implementation from Step 3]

## Data Mapping
[Mapping definitions from Step 4]

## Pagination
[Pattern from Step 5]

## Sync State & Recovery
[Implementation from Step 6]

## Production Checklist
[From Step 7]

Completion

API Integration: Complete!

Integration: [Source] → [Destination]
Entities synced: [List]
Auth method: [Method]
Rate limit handling: [Strategy]
Pagination: [Pattern]
Sync state: [Checkpoint-based]

Next steps:
1. Implement using the code patterns above
2. Set up credentials in your secrets manager
3. Run an initial full sync with a small dataset
4. Verify data mapping accuracy in destination
5. Enable incremental sync on schedule

Level History

• Lv.1: Base: 6 auth patterns (API key, Bearer, OAuth 2.0 with refresh, JWT, Basic, HMAC), reactive + proactive rate limiting (token bucket), declarative data mapper with transformations, 4 pagination patterns with generic async generator, incremental sync with checkpoint-based recovery, sync state table, production checklist. (Origin: MemStack Pro v3.2, Mar 2026)