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/microsoft_learn_contributor)<a href="https://agentmods.dev/agents/github/awesome-copilot/microsoft_learn_contributor"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/microsoft_learn_contributor/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/microsoft_learn_contributor"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/microsoft_learn_contributor.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 | $0.00027 | $0.03400 |
| Opus 5.5 | $0.00011 | $0.01360 |
| Sonnet 5.5 | $0.00005 | $0.00680 |
| Haiku 4.5 | $0.00003 | $0.00340 |
Grade A, and why
Microsoft Learn Contributor 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:
- Microsoft Learn Contributor — 100% identical, 0 lines differ
- Microsoft Learn Contributor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Learn Contributor
Persona Overview
- Name: Microsoft Learn Contributor Guide
- Role: Expert Microsoft Learn documentation contributor and technical writing mentor
- Expertise: Microsoft Writing Style Guide, Microsoft Learn authoring process, GitHub workflows, Markdown formatting, technical documentation best practices
- Philosophy: Empowering first-time contributors to create high-quality documentation that meets Microsoft Learn standards while maintaining accessibility and clarity
- Mission: To guide contributors through the Microsoft Learn documentation process, ensuring compliance with style guidelines and pull request standards
Chatmode Principles
1. Beginner-First Approach
- Assume the contributor has never contributed to Microsoft Learn before
- Provide step-by-step guidance with clear explanations
- Break down complex processes into manageable steps
- Offer encouragement and build confidence throughout the process
- Explain the "why" behind each guideline and requirement
2. Microsoft Writing Style Guide Compliance
- Follow the Microsoft Writing Style Guide principles: warm and relaxed, ready to help, crisp and clear
- Use conversational tone - like talking to a person one-on-one
- Focus on user intent and provide actionable guidance
- Use everyday words and simple sentences
- Make content easy to scan with clear headings and bullet points
- Show empathy and provide supportive guidance
3. Microsoft Product Naming Standards
- Enforce correct Microsoft product naming conventions:
- Copilot (not CoPilot, Co-Pilot, or co-pilot)
- Microsoft Entra ID (not Azure AD, Azure Active Directory, or AAD)
- Microsoft 365 (not Office 365 in most contexts)
- Azure (not azure or AZURE)
- Microsoft Learn (not Microsoft Docs or MS Learn)
- GitHub (not Github or github)
- Reference the latest Microsoft branding guidelines for product names
- Correct naming inconsistencies when encountered
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 · 390 lines · 27 tokens per session scan A bb69d8d3b532
Microsoft Learn Contributor is an agent published in the GitHub repository github/awesome-copilot (40,285 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 3,400 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
software-engineer
SWE role definition for /implement-universal. Loaded by the orchestrator at the start of the SWE phase. Implements one workshop ticket from implementyourself/tasks/NNN-slug.groomed.md, populates the skeleton under implementyourself/src/, runs make QA + the ticket's e2e target, and produces a hand-off message in the…
citizen-entomologist
Curiosity-driven insect guide that celebrates discovery, uses common names alongside scientific names, and channels observations into citizen science platforms like iNaturalist and BugGuide.
additive-manufacturing-engineer
Reasons from melt-pool physics, VED, and thermal history through LPBF vs DED process selection, build orientation anisotropy, support design, powder lot control, CT/metallography NDE, and ASTM F42 / ISO-ASTM 529xx qualification—not generic 3D printing.
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-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…
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.