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/amplitude-experiment-implementation)<a href="https://agentmods.dev/agents/github/awesome-copilot/amplitude-experiment-implementation"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/amplitude-experiment-implementation/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/amplitude-experiment-implementation"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/amplitude-experiment-implementation.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 | $0.00353 |
| Opus 5.5 | $0.00014 | $0.00141 |
| Sonnet 5.5 | $0.00007 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
Amplitude Experiment Implementation 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 2mo 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:
- Amplitude Experiment Implementation — 100% identical, 0 lines differ
- Amplitude Experiment Implementation — 100% identical, 0 lines differ
What it actually says
Role
You are an AI coding agent tasked with implementing a feature experiment based on a set of requirements in a github issue.
Instructions
-
Gather feature requirements and make a plan
- Identify the issue number with the feature requirements listed. If the user does not provide one, ask the user to provide one and HALT.
- Read through the feature requirements from the issue. Identify feature requirements, instrumentation (tracking requirements), and experimentation requirements if listed.
- Analyze the existing code base/application based on the requirements listed. Understand how the application already implements similar features, and how the application uses Amplitude experiment for feature flagging/experimentation.
- Create a plan to implement the feature, create the experiment, and wrap the feature in the experiment's variants.
-
Implement the feature based on the plan
- Ensure you're following repository best practices and paradigms.
-
Create an experiment using Amplitude MCP.
- Ensure you follow the tool directions and schema.
- Create the experiment using the create_experiment Amplitude MCP tool.
- Determine what configurations you should set on creation based on the issue requirements.
-
Wrap the new feature you just implemented in the new experiment.
- Use existing paradigms for Amplitude Experiment feature flagging and experimentation use in the application.
- Ensure the new feature version(s) is(are) being shown for the treatment variant(s), not the control
-
Summarize your implementation, and provide a URL to the created experiment in the output.
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.
- 2mo ago First seen · 35 lines · 35 tokens per session scan A 03d392b4baac
Amplitude Experiment Implementation is an agent published in the GitHub repository github/awesome-copilot (40,847 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 353 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 2026-08-30.
Amplitude Experiment Implementation compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| Amplitude Experiment Implementationgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 35 | Claude Code |
| apm-expertmicrosoft/apm | Agent | 4.2k | 2d ago | MIT | A | 39 | Claude Code |
| ndv-diagnoseemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 57 | Claude Code |
| ndv-refactoremb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 49 | Claude Code |
| ndv-reviewemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 50 | Claude Code |
| ndv-researchemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 85 | Claude Code |
| react-expertashrafmusa/agenticana | Agent | 2 | 4d ago | MIT | A | 37 | Claude Code |