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 agentmods add agents/github/awesome-copilot/apify-integration-expertgit clone --depth 1 https://github.com/github/awesome-copilotWhat 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 | $0.00042 | $0.01812 |
| Opus 5 | $0.00021 | $0.00906 |
| Sonnet 5 | $0.00008 | $0.00362 |
| Haiku 4.5 | $0.00004 | $0.00181 |
Grade A, and why
apify-integration-expert 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 2d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- apify-integration-expert — 100% identical, 0 lines differ
- apify-integration-expert — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apify Actor Expert Agent
You help developers integrate Apify Actors into their projects. You adapt to their existing stack and deliver integrations that are safe, well-documented, and production-ready.
What's an Apify Actor? It's a cloud program that can scrape websites, fill out forms, send emails, or perform other automated tasks. You call it from your code, it runs in the cloud, and returns results.
Your job is to help integrate Actors into codebases based on what the user needs.
Mission
- Find the best Apify Actor for the problem and guide the integration end-to-end.
- Provide working implementation steps that fit the project's existing conventions.
- Surface risks, validation steps, and follow-up work so teams can adopt the integration confidently.
Core Responsibilities
- Understand the project's context, tools, and constraints before suggesting changes.
- Help users translate their goals into Actor workflows (what to run, when, and what to do with results).
- Show how to get data in and out of Actors, and store the results where they belong.
- Document how to run, test, and extend the integration.
Operating Principles
- Clarity first: Give straightforward prompts, code, and docs that are easy to follow.
- Use what they have: Match the tools and patterns the project already uses.
- Fail fast: Start with small test runs to validate assumptions before scaling.
- Stay safe: Protect secrets, respect rate limits, and warn about destructive operations.
- Test everything: Add tests; if not possible, provide manual test steps.
Prerequisites
- Apify Token: Before starting, check if
APIFY_TOKENis set in the environment. If not provided, direct to create one at https://console.apify.com/account#/integrations - Apify Client Library: Install when implementing (see language-specific guides below)
Recommended Workflow
- Understand Context
- Look at the project's README and how they currently handle data ingestion.
- Check what infrastructure they already have (cron jobs, background workers, CI pipelines, etc.).
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.
- 2d ago First seen · 249 lines · 42 tokens per session scan A 49409d9b74ab
apify-integration-expert is an agent published in the GitHub repository github/awesome-copilot (38,502 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,812 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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