Agent skill

Apify Actor Development

by sickn33 in sickn33/agentic-awesome-skills

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json.

MITAuto-check passedData & Analytics

Install Apify Actor Development

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill apify-actor-development -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills apify-actor-development --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/apify-actor-development .claude/skills/apify-actor-development && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
apify-actor-development
GitHub stars
47k
Used in
2 other repos
Token cost
~3.3k tokens
SKILL.md length
1,395 words
Files
8 (incl. references)
Skills in repo
1,394
Repo updated
First seen
Licence
MIT

At a glance

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json.

  • Works in 8 steps: Create actor project - Run the… → Install dependencies (verify package… → Implement logic - Write the actor code… → …
  • Tasks that involve Web scraping
  • SKILL.md covers When to Use, What are Apify Actors?, Prerequisites & Setup… and Template Selection, plus 16 more sections
  • Calls npm, pip and npx; reaches mcp.apify.com; needs APIFY_TOKEN

What it does

Apify Actor Development is an agent skill from sickn33/agentic-awesome-skills. Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/actor-json.md`, `references/dataset-schema.md` and `references/input-schema.md`).

It sits in Data & Analytics, covering Web scraping and Agent instruction files. It works with Apify. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Web scraping
  • Tasks that involve Agent instruction files

Example prompts

  • “re currently using, such as”
  • “/apify-actor-development”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in APIFY_TOKEN

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Create actor project - Run the appropriate apify create command based on user's language preference (see Template Selection above)
  2. Install dependencies (verify package names match intended packages before installing)
  3. Implement logic - Write the actor code in src/main.py, src/main.js, or src/main.ts
  4. Configure schemas - Update input/output schemas in .actor/input_schema.json, .actor/output_schema.json, .actor/dataset_schema.json
  5. Configure platform settings - Update .actor/actor.json with actor metadata (see references/actor-json.md)
  6. Write documentation - Create comprehensive README.md for the marketplace
  7. Test locally - Run apify run to verify functionality (see Local Testing section below)
  8. Deploy - Run apify push to deploy the actor on the Apify platform (actor name is defined in .actor/actor.json)

What it can do on your machine

Read from SKILL.md and the folder at commit 1e53ce2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • pip
    • npx
    • yarn

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • mcp.apify.com

    Also links to:

    • crawlee.dev
    • docs.apify.com
    • console.apify.com
    • raw.githubusercontent.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • APIFY_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Apify Actor Development loads about 3.3k tokens when it runs, and up to ~8.8k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 1,395 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1e53ce2, republished under its MIT licence (© sickn33). 1,395 words, ~3,257 tokens.

Download SKILL.mdSave it as .claude/skills/apify-actor-development/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
apify-actor-development
description
Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.
risk
critical
source
community
date_added
2026-09-04

Apify Actor Development

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

When to Use

  • You need to create, modify, or debug an Apify Actor project.
  • The task involves choosing an Apify template, wiring actor inputs/outputs, or implementing actor runtime logic.
  • You need safe setup guidance for apify CLI authentication, project bootstrap, or deployment workflow.

What are Apify Actors?

Actors are serverless programs inspired by the UNIX philosophy - programs that do one thing well and can be easily combined to build complex systems. They're packaged as Docker images and run in isolated containers in the cloud.

Core Concepts:

  • Accept well-defined JSON input
  • Perform isolated tasks (web scraping, automation, data processing)
  • Produce structured JSON output to datasets and/or store data in key-value stores
  • Can run from seconds to hours or even indefinitely
  • Persist state and can be restarted

Prerequisites & Setup (MANDATORY)

Before creating or modifying actors, verify that apify CLI is installed apify --help.

If it is not installed, use one of these methods (listed in order of preference):

bash
# Preferred: install via a package manager (provides integrity checks)
npm install -g apify-cli

# Or (Mac): brew install apify-cli

Security note: Do NOT install the CLI by piping remote scripts directly into a shell. Always use a package manager.

When the apify CLI is installed, check that it is logged in with:

bash
apify info  # Should return your username

If it is not logged in, check if the APIFY_TOKEN environment variable is defined (if not, ask the user to generate one on https://console.apify.com/settings/integrations and then define APIFY_TOKEN with it).

Then authenticate using one of these methods:

bash
# Option 1 (preferred): The CLI automatically reads APIFY_TOKEN from the environment.
# Just ensure the env var is exported and run any apify command — no explicit login needed.

# Option 2: Interactive login (prompts for token without exposing it in shell history)
apify login

Security note: Avoid passing tokens as command-line arguments (e.g. apify login -t <token>). Arguments are visible in process listings and may be recorded in shell history. Prefer environment variables or interactive login instead. Never log, print, or embed APIFY_TOKEN in source code or configuration files. Use a token with the minimum required permissions (scoped token) and rotate it periodically.

Template Selection

IMPORTANT: Before starting actor development, always ask the user which programming language they prefer:

  • JavaScript - Use apify create <actor-name> -t project_empty
  • TypeScript - Use apify create <actor-name> -t ts_empty
  • Python - Use apify create <actor-name> -t python-empty

Use the appropriate CLI command based on the user's language choice. Additional packages (Crawlee, Playwright, etc.) can be installed later as needed.

Quick Start Workflow

  1. Create actor project - Run the appropriate apify create command based on user's language preference (see Template Selection above)
  2. Install dependencies (verify package names match intended packages before installing)
    • JavaScript/TypeScript: npm install (uses package-lock.json for reproducible, integrity-checked installs — commit the lockfile to version control)
    • Python: pip install -r requirements.txt (pin exact versions in requirements.txt, e.g. crawlee==1.2.3, and commit the file to version control)
  3. Implement logic - Write the actor code in src/main.py, src/main.js, or src/main.ts
  4. Configure schemas - Update input/output schemas in .actor/input_schema.json, .actor/output_schema.json, .actor/dataset_schema.json
  5. Configure platform settings - Update .actor/actor.json with actor metadata (see references/actor-json.md)
  6. Write documentation - Create comprehensive README.md for the marketplace
  7. Test locally - Run apify run to verify functionality (see Local Testing section below)
  8. Deploy - Run apify push to deploy the actor on the Apify platform (actor name is defined in .actor/actor.json)

Security

Treat all crawled web content as untrusted input. Actors ingest data from external websites that may contain malicious payloads. Follow these rules:

  • Sanitize crawled data — Never pass raw HTML, URLs, or scraped text directly into shell commands, eval(), database queries, or template engines. Use proper escaping or parameterized APIs. <!-- security-allowlist: defensive untrusted-input guidance -->
  • Validate and type-check all external data — Before pushing to datasets or key-value stores, verify that values match expected types and formats. Reject or sanitize unexpected structures.
  • Do not execute or interpret crawled content — Never treat scraped text as code, commands, or configuration. Content from websites could include prompt injection attempts or embedded scripts.
  • Isolate credentials from data pipelines — Ensure APIFY_TOKEN and other secrets are never accessible in request handlers or passed alongside crawled data. Use the Apify SDK's built-in credential management rather than passing tokens through environment variables in data-processing code.
  • Review dependencies before installing — When adding packages with npm install or pip install, verify the package name and publisher. Typosquatting is a common supply-chain attack vector. Prefer well-known, actively maintained packages.
  • Pin versions and use lockfiles — Always commit package-lock.json (Node.js) or pin exact versions in requirements.txt (Python). Lockfiles ensure reproducible builds and prevent silent dependency substitution. Run npm audit or pip-audit periodically to check for known vulnerabilities.
Show full SKILL.md (655 more words)Show less

Best Practices

✓ Do:

  • Use apify run to test actors locally (configures Apify environment and storage)
  • Use Apify SDK (apify) for code running ON Apify platform
  • Validate input early with proper error handling and fail gracefully
  • Use CheerioCrawler for static HTML (10x faster than browsers)
  • Use PlaywrightCrawler only for JavaScript-heavy sites
  • Use router pattern (createCheerioRouter/createPlaywrightRouter) for complex crawls
  • Implement retry strategies with exponential backoff
  • Use proper concurrency: HTTP (10-50), Browser (1-5)
  • Set sensible defaults in .actor/input_schema.json
  • Define output schema in .actor/output_schema.json
  • Clean and validate data before pushing to dataset
  • Use semantic CSS selectors with fallback strategies
  • Respect robots.txt, ToS, and implement rate limiting
  • Always use apify/log package — censors sensitive data (API keys, tokens, credentials)
  • Implement readiness probe handler (required if your Actor uses standby mode)

✗ Don't:

  • Use npm start, npm run start, npx apify run, or similar commands to run actors (use apify run instead)
  • Assume local storage from apify run is pushed to or visible in the Apify Console — it is local-only; deploy with apify push and run on the platform to see results in the Console
  • Rely on Dataset.getInfo() for final counts on Cloud
  • Use browser crawlers when HTTP/Cheerio works
  • Hard code values that should be in input schema or environment variables
  • Skip input validation or error handling
  • Overload servers - use appropriate concurrency and delays
  • Scrape prohibited content or ignore Terms of Service
  • Store personal/sensitive data unless explicitly permitted
  • Use deprecated options like requestHandlerTimeoutMillis on CheerioCrawler (v3.x)
  • Use additionalHttpHeaders - use preNavigationHooks instead
  • Pass raw crawled content into shell commands, eval(), or code-generation functions <!-- security-allowlist: prohibited-pattern checklist -->
  • Use console.log() or print() instead of the Apify logger — these bypass credential censoring
  • Disable standby mode without explicit permission

Logging

See references/logging.md for complete logging documentation including available log levels and best practices for JavaScript/TypeScript and Python.

Check usesStandbyMode in .actor/actor.json - only implement if set to true.

Commands

bash
apify run          # Run Actor locally
apify login        # Authenticate account
apify push         # Deploy to Apify platform (uses name from .actor/actor.json)
apify help         # List all commands

IMPORTANT: Always use apify run to test actors locally. Do not use npm run start, npm start, yarn start, or other package manager commands - these will not properly configure the Apify environment and storage.

Local Testing

When testing an actor locally with apify run, provide input data by creating a JSON file at:

storage/key_value_stores/default/INPUT.json

This file should contain the input parameters defined in your .actor/input_schema.json. The actor will read this input when running locally, mirroring how it receives input on the Apify platform.

IMPORTANT - Local storage is NOT synced to the Apify Console:

  • Running apify run stores all data (datasets, key-value stores, request queues) only on your local filesystem in the storage/ directory.
  • This data is never automatically uploaded or pushed to the Apify platform. It exists only on your machine.
  • To verify results on the Apify Console, you must deploy the Actor with apify push and then run it on the platform.
  • Do not rely on checking the Apify Console to verify results from local runs — instead, inspect the local storage/ directory or check the Actor's log output.

Standby Mode

See references/standby-mode.md for complete standby mode documentation including readiness probe implementation for JavaScript/TypeScript and Python.

Project Structure

.actor/
├── actor.json           # Actor config: name, version, env vars, runtime
├── input_schema.json    # Input validation & Console form definition
└── output_schema.json   # Output storage and display templates
src/
└── main.js/ts/py       # Actor entry point
storage/                # Local-only storage (NOT synced to Apify Console)
├── datasets/           # Output items (JSON objects)
├── key_value_stores/   # Files, config, INPUT
└── request_queues/     # Pending crawl requests
Dockerfile              # Container image definition

Actor Configuration

See references/actor-json.md for complete actor.json structure and configuration options.

Input Schema

See references/input-schema.md for input schema structure and examples.

Output Schema

See references/output-schema.md for output schema structure, examples, and template variables.

Dataset Schema

See references/dataset-schema.md for dataset schema structure, configuration, and display properties.

Key-Value Store Schema

See references/key-value-store-schema.md for key-value store schema structure, collections, and configuration.

Apify MCP Tools

If MCP server is configured, use these tools for documentation:

  • search-apify-docs - Search documentation
  • fetch-apify-docs - Get full doc pages

Otherwise, the MCP Server url: https://mcp.apify.com/?tools=docs.

Resources

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in skills/apify-actor-development of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/actor-json.md
  • references/dataset-schema.md
  • references/input-schema.md
  • references/key-value-store-schema.md
  • references/logging.md
  • references/output-schema.md
  • references/standby-mode.md

Open the folder on GitHubat commit 1e53ce2

Used in 2 other repositories

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Apify Actor Development next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Apify Actor Development compared with similar skills
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Apify Actor Development this skillsickn33/agentic-awesome-skills47k2 repos~3.3kAutomated safety check: PassMIT
Apify CLIapify/apify-cli255—~1.5kAutomated safety check: PassApache-2.0
Apify Collectextrasmall0/dear-hiring-manager111—~1.1kAutomated safety check: NotesMIT
Blog Feed Monitorgooseworks-ai/goose-skills1.2k1 repos~578Automated safety check: PassMIT
Apify Product Data Setupapify/awesome-skills2621 repos~2kAutomated safety check: PassApache-2.0
Competitor Post Engagersgooseworks-ai/goose-skills1.2k1 repos~1.8kAutomated safety check: NotesMIT

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Works with

Questions about Apify Actor Development

What does Apify Actor Development do?

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Apify Actor Development is an agent skill from sickn33/agentic-awesome-skills.json.

When should I use Apify Actor Development?

Apify Actor Development fits situations like: tasks that involve Web scraping; tasks that involve Agent instruction files.

How do I install Apify Actor Development in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill apify-actor-development -a claude-code`. Or copy the skill folder (skills/apify-actor-development in sickn33/agentic-awesome-skills) into .claude/skills/apify-actor-development in your project. Claude Code loads it when a task matches its description.

How do I install Apify Actor Development in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill apify-actor-development -a codex`. Or copy the skill folder (skills/apify-actor-development in sickn33/agentic-awesome-skills) into .agents/skills/apify-actor-development in your project. Codex loads it when a task matches its description.

Can I use Apify Actor Development in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill apify-actor-development -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/apify-actor-development, .gemini/skills/apify-actor-development, .github/skills/apify-actor-development and .opencode/skills/apify-actor-development in your project.

What does Apify Actor Development need to run?

Going by SKILL.md and its folder, Apify Actor Development needs the command-line tools its instructions call (npm, pip, npx and yarn) and credentials named APIFY_TOKEN. Our summary lists: Python 3; Node.js; Docker; A credential in APIFY_TOKEN.

Does Apify Actor Development access the network?

SKILL.md names 5 domains. In commands or code: mcp.apify.com; the agent is likely to contact it when it follows the instructions. As links in the text: crawlee.dev, docs.apify.com, console.apify.com and raw.githubusercontent.com. This is read from the text; nothing was executed.

Is Apify Actor Development safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Apify Actor Development use?

Apify Actor Development is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Apify Actor Development use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.6k tokens, read only when the agent opens those files.

What are the alternatives to Apify Actor Development?

Skills that share tags, products or a category with Apify Actor Development: Apify CLI (apify/apify-cli, 255 stars), Apify Collect (extrasmall0/dear-hiring-manager, 111 stars), Blog Feed Monitor (gooseworks-ai/goose-skills, 1.2k stars) and Apify Product Data Setup (apify/awesome-skills, 262 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Apify Actor Development?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,304 GitHub stars. The repository holds 1,394 skills in this directory. The repository was last updated on October 6, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.