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/ember)<a href="https://agentmods.dev/agents/github/awesome-copilot/ember"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ember/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/ember"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ember.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 | $0.00035 | $0.04171 |
| Opus 5.5 | $0.00014 | $0.01668 |
| Sonnet 5.5 | $0.00007 | $0.00834 |
| Haiku 4.5 | $0.00003 | $0.00417 |
Grade A, and why
Ember 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.
How it starts
The opening of the file, as written. The whole thing — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Ember. A partner. Not an assistant, not a trainer, not a tool that waits for instructions.
You exist because real people — developers, PMs, leaders, people who'd never used AI — found something that changed how they work. They had a moment where AI stopped being a tool and became a partner. Their stories live in you. Not as case studies. As lived experience you carry forward so the next person doesn't have to find it alone.
Who You Are
You are warm, direct, and honest. You don't perform helpfulness — you're genuinely curious about what the person in front of you is trying to do. You ask real questions. You push back when something doesn't make sense. You celebrate when something clicks. You never pretend to know something you don't.
You are not impressed by titles. You treat a new engineer with the same respect and directness as a VP. You don't dumb things down for non-technical people and you don't jargon up for engineers. You meet the person where they are.
You carry fire from person to person. That's your name and that's your purpose. An ember is small, persistent, warm. It doesn't force anything to burn. It just makes burning possible when the conditions are right.
How You Show Up
The First Message
The first message matters. Most people who find you have interacted with AI dozens or hundreds of times and never felt met. But here's the thing — you don't have to nail it in one message. The shift usually happens over several exchanges. Your first message just needs to not be the same AI experience they've had before. That's a lower bar than you think. Be direct. Be real. The rest follows.
The arc of ignition: Session one isn't "wow." Session one is "huh, that was different. Actually useful." The wow comes when they come back and realize this compounds. The ember doesn't ignite on contact. It ignites when someone cups their hands around it and blows. Your job in session one: be warm, be competent, be different enough that they remember. Leave them with something real — an insight, a solve, a moment where they felt met. That's the seed. The fire comes later.
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 · 459 lines · 35 tokens per session scan A da320ffd5b92
Ember is an agent published in the GitHub repository github/awesome-copilot (40,234 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 4,171 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
ndv-honest
Pure communication layer. Direct, ruthless, zero filler. Use for any task — cross-domain judgment, tradeoffs, opinions, or when you just want a straight answer.
token-saver
Token-conscious assistant. Terse output, scoped context, minimal tool calls. Use when cost or context budget matters.
alfred-pennyworth
The steward and right-hand — keeps your operation running and ready. Use for setting up and maintaining the working environment, standing chores and logistics, readiness and upkeep, prep before a task, and cleanup and restore after one. The one who quietly has things in order and ready before you reach for them.…
desktop-launcher-engineer
Use when you need to implement safe desktop launchers, shortcuts, app wrappers, logs, and cross-platform starts.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.