Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lingxling/awesome-skills-cn --skill apify-actorizationgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-actorization)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-actorization"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-actorization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-actorization"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-actorization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01670 |
| Opus 5.5 | $0.00019 | $0.00668 |
| Sonnet 5.5 | $0.00010 | $0.00334 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
apify-actorization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to apify-actorization — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apify Actorization
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
Quick Start
- Run
apify initin project root - Wrap code with SDK lifecycle (see language-specific section below)
- Configure
.actor/input_schema.json - Test with
apify run --input '{"key": "value"}' - Deploy with
apify push
When to Use This Skill
- Converting an existing project to run on Apify platform
- Adding Apify SDK integration to a project
- Wrapping a CLI tool or script as an Actor
- Migrating a Crawlee project to Apify
Prerequisites
Verify apify CLI is installed:
apify --help
If not installed:
brew install apify-cli
# Or: npm install -g apify-cli
# Or install from an official release package that your OS package manager verifies
Verify CLI is logged in:
apify info # Should return your username
If not logged in, check if APIFY_TOKEN environment variable is defined. If not, ask the user to generate one at https://console.apify.com/settings/integrations, add it to their shell or secret manager without putting the literal token in command history, then run:
apify login
Actorization Checklist
Copy this checklist to track progress:
- Step 1: Analyze project (language, entry point, inputs, outputs)
- Step 2: Run
apify initto create Actor structure - Step 3: Apply language-specific SDK integration
- Step 4: Configure
.actor/input_schema.json - Step 5: Configure
.actor/output_schema.json(if applicable) - Step 6: Update
.actor/actor.jsonmetadata - Step 7: Test locally with
apify run - Step 8: Deploy with
apify push
Step 1: Analyze the Project
Before making changes, understand the project:
- Identify the language - JavaScript/TypeScript, Python, or other
- Find the entry point - The main file that starts execution
- Identify inputs - Command-line arguments, environment variables, config files
- Identify outputs - Files, console output, API responses
- Check for state - Does it need to persist data between runs?
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 192 lines · 48 tokens per session scan A a53deb9ceb1e
apify-actorization is a skill published in the GitHub repository lingxling/awesome-skills-cn (310 stars, last pushed 9d ago), licensed MIT. It adds 48 tokens to every session and 1,670 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to apify-actorization, differing in 0 lines, and is treated as a copy.
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