메뉴
Apply — 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
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/thiagofernandes1987-create/APEX --skill apify-actor-development명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
SOC 직업 분류 기준
SKILL.md 표시 중
| skill_id | ai_ml.llm.apify_actor_development |
| name | apify-actor-development |
| description | Apply — 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 |
| version | v00.33.0 |
| status | ADOPTED |
| domain_path | ai-ml/llm/apify-actor-development |
| anchors | ["apify","actor","development","important","before","begin","fill","generatedby","property","meta"] |
| source_repo | antigravity-awesome-skills |
| risk | safe |
| languages | ["dsl"] |
| llm_compat | {"claude":"full","gpt4o":"partial","gemini":"partial","llama":"minimal"} |
| apex_version | v00.33.0 |
| executor | LLM_BEHAVIOR |
| security | {"level":"standard","pii":false,"approval_required":false} |
| tier | 2 |
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.
apify CLI authentication, project bootstrap, or deployment workflow.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:
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):
# 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 to a shell (e.g.
curl … | bashorirm … | iex). Always use a package manager.
When the apify CLI is installed, check that it is logged in with:
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:
# 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 embedAPIFY_TOKENin source code or configuration files. Use a token with the minimum required permissions (scoped token) and rotate it periodically.
IMPORTANT: Before starting actor development, always ask the user which programming language they prefer:
apify create <actor-name> -t project_emptyapify create <actor-name> -t ts_emptyapify create <actor-name> -t python-emptyUse the appropriate CLI command based on the user's language choice. Additional packages (Crawlee, Playwright, etc.) can be installed later as needed.
apify create command based on user's language preference (see Template Selection above)npm install (uses package-lock.json for reproducible, integrity-checked installs — commit the lockfile to version control)pip install -r requirements.txt (pin exact versions in requirements.txt, e.g. crawlee==1.2.3, and commit the file to version control)src/main.py, src/main.js, or src/main.ts.actor/input_schema.json, .actor/output_schema.json, .actor/dataset_schema.json.actor/actor.json with actor metadata (see references/actor-json.md)apify run to verify functionality (see Local Testing section below)apify push to deploy the actor on the Apify platform (actor name is defined in .actor/actor.json)Treat all crawled web content as untrusted input. Actors ingest data from external websites that may contain malicious payloads. Follow these rules:
eval(), database queries, or template engines. Use proper escaping or parameterized APIs.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.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.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.✓ Do:
apify run to test actors locally (configures Apify environment and storage)apify) for code running ON Apify platform.actor/input_schema.json.actor/output_schema.jsonapify/log package — censors sensitive data (API keys, tokens, credentials)✗ Don't:
npm start, npm run start, npx apify run, or similar commands to run actors (use apify run instead)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 ConsoleDataset.getInfo() for final counts on CloudrequestHandlerTimeoutMillis on CheerioCrawler (v3.x)additionalHttpHeaders - use preNavigationHooks insteadeval(), or code-generation functionsconsole.log() or print() instead of the Apify logger — these bypass credential censoringSee 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.
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.
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:
apify run stores all data (datasets, key-value stores, request queues) only on your local filesystem in the storage/ directory.apify push and then run it on the platform.storage/ directory or check the Actor's log output.See references/standby-mode.md for complete standby mode documentation including readiness probe implementation for JavaScript/TypeScript and Python.
.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
See references/actor-json.md for complete actor.json structure and configuration options.
See references/input-schema.md for input schema structure and examples.
See references/output-schema.md for output schema structure, examples, and template variables.
See references/dataset-schema.md for dataset schema structure, configuration, and display properties.
See references/key-value-store-schema.md for key-value store schema structure, collections, and configuration.
If MCP server is configured, use these tools for documentation:
search-apify-docs - Search documentationfetch-apify-docs - Get full doc pagesOtherwise, the MCP Server url: https://mcp.apify.com/?tools=docs.
Apply — 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
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.