Apify Automation
by ComposioHQApify Automation is a Claude skill for running Apify Actors through Composio: connect MCP, run sync or async scraping jobs, fetch datasets, create tasks, and inspect logs.
Score: 76/100. This is a solid listing candidate for directory users who want Claude/agents to operate Apify through Composio: the skill has real workflow substance, named tools, setup steps, and practical constraints around Actor schemas. It is not a turnkey package because it has no install command or support files and relies on external Apify Actor documentation, but it should still reduce guesswork compared with a generic prompt.
- Clear trigger and scope: it is explicitly for running Apify web scraping Actors, managing datasets, creating tasks, and retrieving crawl results through the Composio Apify integration.
- Operationally useful workflows are documented, including synchronous Actor execution with dataset retrieval and named tool calls such as `APIFY_RUN_ACTOR_SYNC_GET_DATASET_ITEMS`.
- Setup guidance identifies the required MCP dependency (`rube`), the MCP endpoint, account connection flow, and the need to check Apify Store schemas.
- No install command or repository README/supporting references are present beyond the single SKILL.md, so setup depends on users already understanding how to add the Rube/Composio MCP server.
- Actor inputs are intentionally delegated to each Apify Actor's own schema, which is correct but means agents may still need to inspect external Actor documentation before execution.
Overview of Apify Automation skill
What Apify Automation does
Apify Automation is a Claude skill for running Apify web scraping Actors through the Composio Apify integration. It lets an agent start Actors, pass Actor-specific JSON input, retrieve dataset items, create reusable tasks, and inspect run logs from the same workflow instead of switching between Claude, Apify Console, and local scripts.
Best-fit users and jobs
This Apify Automation skill is best for users who already know what site or data source they need to scrape and want an AI agent to operate Apify safely and repeatably. It fits lead collection, product monitoring, search results extraction, directory scraping, social/profile enrichment, and other structured data jobs where an Apify Actor already exists or can be configured from the Apify Store.
Key differentiators for web scraping
The useful difference from a generic scraping prompt is tool access. The skill is designed around concrete Apify operations such as APIFY_RUN_ACTOR_SYNC_GET_DATASET_ITEMS, asynchronous Actor runs, dataset retrieval, task creation, and log inspection. For Apify Automation for Web Scraping, the main value is not writing scraping code; it is helping the agent choose the right Actor, pass valid schema input, and return usable output.
Adoption constraints to check first
The skill requires the Composio MCP server rube and an authenticated Apify account. It does not replace Actor documentation: every Actor has its own input schema, limits, pricing, and output format. If you cannot connect Apify, cannot use MCP tools, or need a custom scraper that is not available as an Actor, this skill may not be enough by itself.
How to Use Apify Automation skill
Apify Automation install and setup path
To install from the skill directory, use:
npx skills add ComposioHQ/awesome-claude-skills --skill "Apify Automation"
Then configure the Composio MCP endpoint:
https://rube.app/mcp
When Claude or your agent asks, connect your Apify account through the authentication link. Before the first real run, open the upstream skill file at composio-skills/apify-automation/SKILL.md and the Actor page at https://apify.com/store for the specific actorId you plan to use.
Inputs the skill needs to run well
A good Apify Automation usage prompt should include: the target Actor ID, the Actor documentation or schema fields, the exact data goal, limits, output format, and whether the run should be synchronous or asynchronous. For example, do not ask “scrape Google Maps.” Ask: “Use Actor compass/crawler-google-places to collect 50 cafes in Austin, return name, address, rating, reviewsCount, and website, use JSON output, and stop after the first dataset page unless more results are needed.”
Workflow for synchronous and async runs
Use synchronous runs when the job is small and you want immediate dataset items in one step. Use asynchronous runs for larger crawls, slower Actors, or jobs where you need to monitor status and fetch results later. A practical flow is: select Actor, validate the input schema, run a small test with limit, inspect dataset shape, adjust fields or search terms, then run the larger job. Ask the agent to show the final Actor input before execution if cost, rate limits, or compliance matter.
Repository files to read first
This repository path is intentionally lean: the important file is SKILL.md. Read its setup section, then its “Core Workflows” examples and tool names. There are no extra resources/, rules/, or helper scripts in the skill folder, so the real operational detail comes from the Apify Actor page and Composio toolkit docs at https://composio.dev/toolkits/apify.
Apify Automation skill FAQ
Is Apify Automation better than a normal Claude prompt?
Yes, when you need Claude to actually operate Apify tools rather than only advise you. A normal prompt can suggest an Actor or draft JSON, but this skill gives the agent a structured path to run Actors, retrieve datasets, and inspect execution logs through Composio. It is most useful when the output must come from a real Apify run.
Do beginners need to know Apify first?
Beginners can use the skill, but they still need to understand three Apify basics: an Actor is the scraper, the Actor input must match its schema, and results usually appear in datasets. The skill reduces tool friction, but it cannot guess undocumented field names reliably. Start with a public Actor that has clear examples and run a small limit first.
When should I not use this skill?
Do not use Apify Automation if the website forbids scraping you cannot lawfully perform, if you need browser automation unrelated to Apify, or if no suitable Actor exists and you are not prepared to build one. It is also a poor fit for one-off questions where a search engine or static API would be simpler, cheaper, and more reliable.
How does it fit an existing scraping stack?
The skill works well as an orchestration layer around Apify, not as a replacement for downstream storage or analytics. You can use it to produce JSON or CSV-like dataset items, then hand results to your database, spreadsheet, enrichment pipeline, or QA process. For production, keep Actor IDs, input JSON, limits, and output field expectations documented outside the chat.
How to Improve Apify Automation skill
Improve Apify Automation prompts with schemas
The biggest quality gain comes from giving the agent the Actor schema or a link to the Actor documentation. Include required fields, optional filters, pagination settings, and any proxy or location options you intend to use. Strong prompt: “Before running, compare my JSON against the Actor schema and list missing or suspicious fields.” This prevents many failed runs.
Reduce failed runs and bad datasets
Common failure modes include invalid actorId, wrong input field names, overbroad searches, low result limits, and assuming all Actors return the same columns. Ask for a small validation run first, then inspect a few dataset items for missing fields, duplicates, and irrelevant records. If results look wrong, change the Actor input rather than only asking for post-processing.
Iterate after the first output
After the first dataset comes back, ask the agent to summarize record count, field coverage, duplicates, errors from logs, and whether the result satisfies the original extraction goal. Then refine: narrow the query, raise or lower limit, add location filters, request a different output format, or switch Actors if the dataset shape is unsuitable.
Add operating rules for repeatable scraping
For recurring jobs, improve Apify Automation by adding your own run checklist: preferred Actors, maximum spend or item limits, required output fields, naming conventions for tasks, and rules for when to use synchronous versus asynchronous execution. These constraints help the agent make consistent decisions and make the skill safer for scheduled or team workflows.