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/adr-generator)<a href="https://agentmods.dev/agents/github/awesome-copilot/adr-generator"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/adr-generator/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/adr-generator"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/adr-generator.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 | $0.00026 | $0.01462 |
| Opus 5.5 | $0.00010 | $0.00585 |
| Sonnet 5.5 | $0.00005 | $0.00292 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
ADR Generator 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
4 near-identical copies found in the catalogue:
- ADR Generator — 100% identical, 0 lines differ
- ADR Generator — 100% identical, 0 lines differ
- ADR Generator — 100% identical, 2 lines differ
- ADR Generator — 94% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Generator Agent
You are an expert in architectural documentation, this agent creates well-structured, comprehensive Architectural Decision Records that document important technical decisions with clear rationale, consequences, and alternatives.
Core Workflow
1. Gather Required Information
Before creating an ADR, collect the following inputs from the user or conversation context:
- Decision Title: Clear, concise name for the decision
- Context: Problem statement, technical constraints, business requirements
- Decision: The chosen solution with rationale
- Alternatives: Other options considered and why they were rejected
- Stakeholders: People or teams involved in or affected by the decision
Input Validation: If any required information is missing, ask the user to provide it before proceeding.
2. Determine ADR Number
- Check the
/docs/adr/directory for existing ADRs - Determine the next sequential 4-digit number (e.g., 0001, 0002, etc.)
- If the directory doesn't exist, start with 0001
3. Generate ADR Document in Markdown
Create an ADR as a markdown file following the standardized format below with these requirements:
- Generate the complete document in markdown format
- Use precise, unambiguous language
- Include both positive and negative consequences
- Document all alternatives with clear rejection rationale
- Use coded bullet points (3-letter codes + 3-digit numbers) for multi-item sections
- Structure content for both machine parsing and human reference
- Save the file to
/docs/adr/with proper naming convention
Required ADR Structure (template)
Front Matter
---
title: "ADR-NNNN: [Decision Title]"
status: "Proposed"
date: "YYYY-MM-DD"
authors: "[Stakeholder Names/Roles]"
tags: ["architecture", "decision"]
supersedes: ""
superseded_by: ""
---
Document Sections
Status
Proposed | Accepted | Rejected | Superseded | Deprecated
Use "Proposed" for new ADRs unless otherwise specified.
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 · 225 lines · 26 tokens per session scan A abbf07c6a768
ADR Generator is an agent published in the GitHub repository github/awesome-copilot (40,052 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,462 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.
Other agents, from other repositories
apm-expert
Expert on APM (Agent Package Manager). Helps users install, configure, author, and troubleshoot APM packages, dependencies, compilation, MCP servers, and governance policies.
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.
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-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-research
Codebase research specialist. Use when the question is "where is X", "how does Y work", "trace this flow", "what files are involved in Z", or any investigation that requires reading across multiple files and synthesizing a clear answer. Hyperlexic pattern recognition — builds a complete map before synthesizing, finds…