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Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harness
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add apify/agent-skills --skill apify-integration-development -a cursor -y
Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.
Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.
Design and build an official Apify integration for a company's product, with minimal help from Apify. This skill covers every integration shape Apify supports - workflow-automation apps, AI agent plugins (coding agents and harnesses), AI framework packages, and direct application clients - so a partner team can ship a first-class Apify integration end to end. The cross-cutting rules below apply to all of them, and one category-specific reference file carries the rest.
Building an official integration? Once you publish it, contact [email protected] so the Apify team can review, test, and validate your integration before it reaches users. We'll check the capability surface, cost controls, error handling, and attribution headers, and help you close any gaps.
Before designing anything, fetch and read https://apify.com/agents.md. It is the canonical quickstart for AI agents and the single source of truth for vocabulary, the run flow, and the cost rule. If the fetch fails, the mini-glossary below keeps the skill usable.
Apify vocabulary (always written with a capital A on the platform):
SUCCEEDED, FAILED, TIMED-OUT, ABORTED).https://apify.com/store.md.https://console.apify.com.Further terms (build, request queue, proxy, pricing models): https://docs.apify.com/llms.txt.
The Apify MCP server is the fastest way to research Actors, schemas, pricing, and docs during integration design. See https://docs.apify.com/integrations/mcp (append .md for a markdown version).
If Apify MCP tools are already available in this environment, use them:
search-actors - find Actors by platform/product keyword (search by product name, not end goal).fetch-actor-details - read an Actor's input schema, output format, README, and pricing before you encode its shape into the integration.search-apify-docs / fetch-apify-docs - pull contextual documentation pages.The anonymous discovery subset (search-actors, fetch-actor-details, search-apify-docs, fetch-apify-docs) works without an account, so you can research even before the developer has connected their token.
Read exactly one reference file based on the product you are integrating into. Each reference carries the category-specific UX design, a canonical capability matrix, and a definition-of-done checklist.
| Product shape | Examples | Read |
|---|---|---|
| Workflow automation platform | Zapier, n8n, Make, Pipedream, Activepieces | references/workflow-automation.md |
| AI agent plugin (coding agent or harness) | Cursor, Claude Code, Codex, GitHub Copilot (coding agents); OpenClaw-style runtimes, Hermes-style harnesses (harnesses) | references/ai-harness-plugin.md |
| AI framework package (PyPI/npm for LLM frameworks) | LangChain, LlamaIndex, Haystack, Vercel AI SDK | references/ai-framework-package.md |
| Application integration (direct client) | A backend service, scheduled job, product feature calling Actors via apify-client or REST | references/sdk-integration.md |
Paths are relative to this skill folder. If your product spans two shapes (e.g. an AI harness built on top of a framework package), read both - the rules compose. The AI agent plugin reference covers two approaches with different trade-offs: a lightweight skills + MCP bundle for skills/MCP-aware coding agents, and a custom tool-registry plugin for OpenClaw/Hermes-style harnesses.
These invariants were extracted from every existing Apify integration. Apply them regardless of shape.
Model the integration's resources on Apify's domain (Actor / Run / Dataset / KV Store / Task). Users coming from Apify Console should find the same concepts under the same names.
Actors can run for seconds to hours. Use the asynchronous flow, never the 300-second synchronous endpoint for anything but short jobs:
POST /v2/actors/{actorId}/runs -> start, return runId
GET /v2/actor-runs/{runId} -> poll until terminal status
GET /v2/datasets/{datasetId}/items -> fetch results on SUCCEEDED
Polling must be bounded: use the run's own timeoutSecs plus a grace buffer, with an absolute ceiling fallback. Never while (true). On a non-terminal status, surface the run ID so the user/agent can poll again or inspect the failure.
Every path that starts a run must expose a cost control. The canonical control is maxTotalChargeUsd (caps the run's total charge on most pricing models) and maxItems (caps billed items on pay-per-result Actors). Send them as options / query parameters, never as Actor input - inside input they are either an Actor-declared field or simply invalid. 0 / empty / null means no limit. For LLM-facing integrations, the ceilings are developer-controlled; an LLM cannot widen them. A Server Actor request such as Web Fetch (below) starts no run and takes neither option.
Stamp an integration header on every outbound request so Apify can attribute traffic: x-apify-integration-platform: <your-platform>. When a request is driven by an AI tool (not a human in a UI), also send x-apify-integration-ai-tool: true. If the integration was built using this skill, add x-apify-integration-origin: apify-integration-development-skill so Apify can distinguish skill-generated integrations from custom ones. One line, big telemetry payoff.
Authorization: Bearer <APIFY_TOKEN>, stored in an env var or secret manager, never hardcoded or logged.Both paths are real - pick by who is present at auth time, not by which is easier.
One base-URL constant, shared between credentials and the HTTP layer. A Server Actor lives on its own host (https://web-fetch.apify.actor) - route it through the same layer so auth and attribution headers still apply. Retries with exponential backoff on 429 and 5xx. Never retry non-idempotent POST /runs on network errors - a duplicate Actor run is a real, billed, side-effecting operation. This is the single most important correctness invariant in the HTTP layer.
Map Apify errors to the host platform's error categories (retryable vs auth vs permanent). Surface the API's actual error text, not a generic HTTP message. For permission-approval failures (a full-permission Actor needs explicit approval), include the approval URL after validating it is an absolute http(s) URL. For LLM consumers, return errors as data (JSON error objects), never as raised exceptions - the model needs something to read and reason about.
When the host supports inbound webhooks, register an Apify webhook scoped to actorId or actorTaskId with the terminal statuses the user picked. Make registration idempotent (a re-activated workflow should not create duplicate webhooks), persist the webhook ID so deactivation can clean it up, and always provide sample/fallback data so users can test the trigger without waiting for a real run.
If the host platform can generate UI fields from an OpenAPI spec, use Apify's spec (https://apify.com/openapi.json) and a tag allowlist. Hand-write only what the spec cannot express: convenience wrappers, bill-cap fields, lean AI-tool output contracts.
Generic "run Actor" serves power users. Add a few opinionated, high-level actions for the common case so non-power users get a 2-field form instead of a full Actor configuration. The canonical one is Web Fetch: one URL in, page content out, built on the apify/web-fetch Server Actor - POST https://web-fetch.apify.actor/ with {"url": "...", "formats": ["markdown"]} returns the content in the response. No run is started, so the run rules above (asynchronous flow, polling, maxTotalChargeUsd) do not apply; the cost is one fetch event per successful request. Three things to get right: always send formats, check fetch.httpStatusCode (HTTP 200 only means Web Fetch itself succeeded), and handle both error shapes - flat {code, error} from the Actor, nested {error: {message}} from the platform - without auto-retrying its 502/504. Full contract: https://apify.com/apify/web-fetch.md. Do not build this on a crawler run with maxCrawlDepth: 0 - that is the legacy "Scrape single URL" pattern.
Keep two test modes: mocked (hermetic, no credentials) and live E2E (real API, CI-gated). Automate releases through the host platform's CI on Git tags / GitHub Releases. Never hand-edit versions or changelogs if a release workflow manages them.
POST /runs on a network error - duplicates a billed run.while (true) polling - ties up the host with no ceiling.maxTotalChargeUsd / maxItems inside Actor input instead of options - silently not a cap.| Purpose | Method + path |
|---|---|
| Start an Actor run | POST /v2/actors/{actorId}/runs |
| Start a Task run | POST /v2/actor-tasks/{taskId}/runs |
| Poll a run | GET /v2/actor-runs/{runId} |
| List runs | GET /v2/actor-runs |
| Dataset items | GET /v2/datasets/{datasetId}/items |
| KV record | GET /v2/key-value-stores/{storeId}/records/{key} |
| Set KV record | PUT /v2/key-value-stores/{storeId}/records/{key} |
| Store search | GET /v2/store |
| Webhook CRUD | POST/GET/DELETE /v2/webhooks |
| Validate token / current user | GET /v2/users/me |
| Fetch one URL (Web Fetch, Server Actor) | POST https://web-fetch.apify.actor/ |
REST reference: https://docs.apify.com/api/v2. OpenAPI spec: https://apify.com/openapi.json.
https://apify.com/agents.md and internalize the model.Real, public integrations per category - read their source when in doubt:
@apify/n8n-nodes-apify (npm), the Apify Zapier app.apify-hermes-agent-plugin (PyPI), @apify/apify-openclaw-plugin.langchain-apify (PyPI).references/sdk-integration.md for the canonical apify-client usage in JS/TS, Python, and over REST.Support for integration questions: [email protected]. Contact us both for design guidance while you build and for review/testing once you publish - we validate the capability surface, cost controls, error handling, and attribution before the integration reaches users.