---
name: api-webhook-integration-advisor
description: "Recommends a production-ready API and webhook integration architecture for connecting SaaS platforms into an existing tech stack — covering integration pattern selection, authentication strategy, webhook reliability design, data sync approach, rate limit handling, error and retry logic, monitoring, and a phased rollout plan. Use when designing a new platform-to-platform integration, choosing between polling and webhooks, hardening a flaky integration, or planning how to connect a martech or ecommerce SaaS into an enterprise stack."
argument-hint: "[source-platform] [target-system]"
allowed-tools: ["Read"]
---

# API & Webhook Integration Advisor

## Purpose
Deliver a concrete integration architecture recommendation for connecting a SaaS platform into an existing tech stack — specifying the integration pattern, auth model, webhook reliability design, sync strategy, and failure handling — so the developer builds something resilient the first time instead of discovering the edge cases in production.

## When to Use
Invoke this skill when the user asks to:
- Design an integration between two SaaS platforms or a SaaS platform and an internal system
- Decide between webhooks, polling, batch sync, or an event stream for a data flow
- Harden a flaky or unreliable existing integration that drops or duplicates events
- Choose an authentication strategy (OAuth, API key, JWT, HMAC) for a connection
- Plan how to connect a martech, CRM, or ecommerce platform into an enterprise stack
- Handle rate limits, retries, idempotency, or webhook signature verification

## Inputs Required
Before starting, collect:
- **Source platform**: The system emitting data or events (e.g., Shopify, HubSpot, Salesforce, Stripe)
- **Target system**: Where the data needs to land (e.g., data warehouse, CDP, internal DB, another SaaS)
- **Data flow direction**: One-way, bidirectional, or fan-out to multiple destinations
- **Data volume & latency needs**: Approximate events/records per day and how fresh the data must be (real-time, near-real-time, hourly, daily)
- **Available integration surfaces**: Which APIs, webhooks, or connectors each platform exposes
- **Auth constraints**: What auth methods each platform supports and any enterprise security requirements
- **Failure tolerance**: What happens if an event is delayed, duplicated, or lost (nice-to-have vs. financial/compliance-critical)

If data volume or latency needs are missing, assume near-real-time at moderate volume and state the assumption so the pattern recommendation can be adjusted.

## Instructions

### Step 1: Select the Integration Pattern
Match the data flow to the right pattern based on latency, volume, and what the platforms actually expose.

- **Webhook (push)**: Best when the source emits events and near-real-time delivery matters. Requires a public HTTPS endpoint, signature verification, and a queue to absorb bursts.
- **Polling (pull)**: Use when the source has no webhooks or webhooks are unreliable/incomplete. Poll on a schedule using a cursor or `updated_since` filter — never full-table scans.
- **Batch/scheduled sync**: Best for high-volume, latency-tolerant flows (nightly warehouse loads, catalog syncs). Cheaper and simpler than real-time.
- **Event stream / pub-sub**: Use when one source must fan out to multiple consumers, or when replay and ordering guarantees matter. Introduce a broker (Kafka, SNS/SQS, Pub/Sub) between source and consumers.
- **Hybrid (webhook + reconciliation poll)**: The production-grade default for critical data — webhooks for speed, a periodic reconciliation poll to catch anything the webhooks missed.

State the chosen pattern and one sentence on why the runner-up was rejected.

### Step 2: Design the Authentication & Authorization Strategy
Pick the auth model per platform and specify token lifecycle handling.

- **OAuth 2.0**: For user-scoped access to platforms like HubSpot, Salesforce, Google. Specify grant type (authorization code for user context, client credentials for server-to-server), token refresh handling, and where tokens are stored (secrets manager, encrypted at rest — never in code).
- **API keys**: For simple server-to-server access. Scope keys to least privilege, rotate on a schedule, store in a secrets manager.
- **HMAC signatures**: For verifying inbound webhook authenticity — compute the signature over the raw request body using the shared secret and compare in constant time. This is mandatory for any public webhook endpoint.
- **JWT / mTLS**: For enterprise stacks requiring stronger identity guarantees.

Call out token expiry handling explicitly — most integration outages are expired-credential failures.

### Step 3: Design Webhook Reliability (if webhooks are used)
Webhooks are fire-and-forget from the sender's side. Make the receiver resilient.

- **Verify then acknowledge fast**: Validate the HMAC signature, enqueue the payload, and return `200` within the platform's timeout (usually 1–5s). Never do heavy processing before responding — that causes the sender to retry and duplicate.
- **Idempotency**: Deduplicate using the event ID or an idempotency key. Store processed IDs so replays and retries don't double-apply.
- **Ordering**: Don't assume events arrive in order. Use timestamps or version numbers on the record, and reject stale updates.
- **Retry & dead-letter**: Failed processing goes to a retry queue with exponential backoff; permanent failures land in a dead-letter queue for inspection — not silent loss.
- **Replay capability**: Persist raw payloads so you can reprocess after a downstream bug fix.

### Step 4: Define the Data Sync & Mapping Approach
Specify how records reconcile between systems.

- **Field mapping**: Enumerate the source-to-target field map, including type coercions and default values for missing fields.
- **Identity resolution**: Define the join key across systems (external ID, email, composite key) and how to handle records with no match.
- **Upsert logic**: Use upsert (insert-or-update) keyed on a stable ID rather than blind inserts to avoid duplicates.
- **Deletes & tombstones**: Decide whether deletes propagate; many platforms only emit soft-delete flags — handle them explicitly.
- **Conflict resolution** (bidirectional only): Pick a strategy — last-write-wins by timestamp, source-of-truth per field, or manual review queue.

### Step 5: Handle Rate Limits & Backpressure
Design for the platform's published limits, not optimistic assumptions.

- **Respect published limits**: Read the source and target rate limits (per-second, per-minute, daily quotas). Implement a token-bucket or leaky-bucket limiter on the client.
- **Honor 429s**: Back off using the `Retry-After` header when present; exponential backoff with jitter otherwise.
- **Batch where supported**: Use bulk endpoints (e.g., Salesforce Bulk API, batch upserts) for high-volume loads instead of per-record calls.
- **Buffer bursts**: Put a queue between ingestion and API writes so traffic spikes don't exceed the target's quota.

### Step 6: Design Error Handling & Observability
Make failures visible and recoverable.

- **Error taxonomy**: Distinguish transient (retry) from permanent (dead-letter + alert) failures. A `500` is retryable; a `422` validation error is not.
- **Structured logging**: Log event ID, correlation ID, source, target, and outcome for every event so a single record can be traced end to end.
- **Alerting thresholds**: Alert on dead-letter queue depth, sustained error rate above a threshold, and delivery lag exceeding the latency SLA — not on every single failure.
- **Reconciliation job**: Schedule a periodic count/checksum comparison between source and target to detect silent drift.

### Step 7: Phased Rollout Plan
Ship the integration in de-risked stages.

- **Phase 1 — Read-only shadow**: Ingest and log without writing to the target. Validate mapping and volume against reality.
- **Phase 2 — Limited write**: Enable writes for a single segment or account; monitor idempotency and error rates.
- **Phase 3 — Full rollout**: Enable all traffic with alerting live and the reconciliation job running.
- **Phase 4 — Hardening**: Add replay tooling, tune rate limits from observed data, and document runbooks for the on-call team.

## Output Format
- **Structure**: 7 sections following the diagnostic sequence, with a one-paragraph architecture summary at the top stating the chosen pattern and auth model
- **Length**: 1,000–1,400 words
- **Format**: Structured document — headed sections with bulleted specifics; field mapping and pattern trade-offs as short tables where they aid scannability
- **Sections**: Architecture summary → Integration pattern → Auth strategy → Webhook reliability → Data sync & mapping → Rate limits & backpressure → Error handling & observability → Phased rollout
- **Tone**: Solution architect — decisive, specific, trade-off aware; recommends one path and names the rejected alternatives with reasons

## Quality Checklist
Before delivering output, verify:
- [ ] A single integration pattern is recommended, with the runner-up explicitly rejected and why
- [ ] The auth strategy names the specific method per platform and addresses token/credential lifecycle
- [ ] If webhooks are used, idempotency, retry/dead-letter, and signature verification are all specified — not just "use webhooks"
- [ ] Rate limit handling references honoring 429s / Retry-After, not just "add rate limiting"
- [ ] The data sync section defines the identity/join key and upsert-vs-insert decision
- [ ] The rollout plan has at least a read-only shadow phase before any writes to the target

## Notes
- The most common production failure is not lost webhooks — it's duplicated events from receivers that process before acknowledging. Prioritize the fast-ack + idempotency pattern.
- For critical data (payments, orders, compliance), always recommend the hybrid webhook + reconciliation-poll pattern; webhooks alone will eventually drop an event.
- If the source platform's webhooks don't include the full payload (only an ID), the design must include a follow-up API fetch — account for the extra rate-limit load this creates.
- Bidirectional integrations are dramatically harder than one-way; if the requirement can be met with one-way sync plus a source of truth, recommend that instead.

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