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-integration-developmentgit 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-integration-development)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-integration-development"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-integration-development/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-integration-development"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-integration-development.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 | $0.00025 | $0.02910 |
| Opus 5.5 | $0.00010 | $0.01164 |
| Sonnet 5.5 | $0.00005 | $0.00582 |
| Haiku 4.5 | $0.00003 | $0.00291 |
Grade A, and why
apify-integration-development 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
100% identical to apify-integration-development — 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- Use when this upstream workflow matches the user's stated goal.
- Use when the task requires the procedures documented in this skill.
Apify Integration Development
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.
Step 0 - Learn the Apify model first (required)
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):
- Actor - a serverless cloud program that takes JSON input, performs a task, and produces structured output. Not an AI agent.
- Actor Run - one execution of an Actor. Each run has its own dataset, key-value store, and request queue, and ends in a terminal status (
SUCCEEDED,FAILED,TIMED-OUT,ABORTED). - Dataset - append-only structured storage for a run's results. An Actor call returns the dataset ID, not its contents.
- Key-Value Store - unstructured/file storage (screenshots, HTML, OUTPUT).
- Actor Task - a saved, parameterized configuration for running an Actor.
- Apify Store - the marketplace of Actors at
https://apify.com/store.md. - Apify Console - the web UI at
https://console.apify.com. - Compute Unit (CU) - billing unit: memory (MB) x duration (hours).
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 · 169 lines · 25 tokens per session scan A 669ca4401e76
apify-integration-development is a skill published in the GitHub repository lingxling/awesome-skills-cn (310 stars, last pushed 9d ago), licensed MIT. It adds 25 tokens to every session and 2,910 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to apify-integration-development, differing in 0 lines, and is treated as a copy.
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