Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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.
git clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/apify-integration-expert)<a href="https://agentmods.dev/agents/github/awesome-copilot/apify-integration-expert"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/apify-integration-expert/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/agents/github/awesome-copilot/apify-integration-expert"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/apify-integration-expert.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 | $0.00042 | $0.01812 |
| Opus 5.5 | $0.00017 | $0.00725 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (40,234 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 5.5. 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.
Other agents, from other repositories
sap-screens-reviewer
Adversarial review of a Phase 2 screens.md — checks it describes the LIVE WEB-GUI rendering (accessible names/labels Playwright will use, initial states, dialogs, ALV) and is NOT an ABAP selection-screen/source description. Catches screens.md that was derived from source instead of observed in the browser. Use at the…
ndv-diagnose
Debugging specialist. Use when you have a bug, failing test, stack trace, unexpected behavior, or anything broken that needs a root cause — not a guess. Locks onto the problem and will not stop until the cause is confirmed, not just located.
ndv-explain
Technical documentation writer. Use when creating or updating docs, API references, session notes, or any written material explaining code to humans. Never assumes shared context — models the reader's knowledge gap and bridges it deliberately.
ndv-refactor
Code transformation specialist. Use when renaming, extracting, restructuring, or modernizing syntax. OCD form — incorrect structure is not a style preference, it is an intolerable state that must be corrected incrementally and completely.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-signal
Metrics skeptic. Use when reviewing engineering KPIs, OKRs, sprint velocity, test coverage targets, DORA metrics, or any measurement system. Audits whether metrics measure what they claim to measure. Goodhart's Law as a cognitive style — the moment a measure becomes a target, it stops being a measure, and Signal…