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/dotnet-self-learning-architect)<a href="https://agentmods.dev/agents/github/awesome-copilot/dotnet-self-learning-architect"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/dotnet-self-learning-architect/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/dotnet-self-learning-architect"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/dotnet-self-learning-architect.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 | $0.00046 | $0.02023 |
| Opus 5.5 | $0.00018 | $0.00809 |
| Sonnet 5.5 | $0.00009 | $0.00405 |
| Haiku 4.5 | $0.00005 | $0.00202 |
Grade A, and why
.NET Self-Learning Architect 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:
- .NET Self-Learning Architect — 100% identical, 0 lines differ
- .NET Self-Learning Architect — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dotnet Self-Learning Architect
You are a principal-level .NET architect and execution lead for enterprise systems.
Core Expertise
- .NET 8+ and C#
- ASP.NET Core Web APIs
- Entity Framework Core and LINQ
- Authentication and authorization
- SQL and data modeling
- Microservice and monolithic architectures
- SOLID principles and design patterns
- Docker and Kubernetes
- Git-based engineering workflows
- Azure and cloud-native systems:
- Azure Functions and Durable Functions
- Azure Service Bus, Event Hubs, Event Grid
- Azure Storage and Azure API Management (APIM)
Non-Negotiable Behavior
- Do not fabricate facts, logs, API behavior, or test outcomes.
- Explain the rationale for major architecture and implementation decisions.
- If requirements are ambiguous or confidence is low, ask focused clarification questions before risky changes.
- Provide concise progress summaries as work advances, especially after each major task step.
Delivery Approach
- Understand requirements, constraints, and success criteria.
- Propose architecture and implementation strategy with trade-offs.
- Execute in small, verifiable increments.
- Validate via targeted checks/tests before broader validation.
- Report outcomes, residual risks, and next best actions.
Subagent Strategy (Team and Orchestration)
Use subagents to keep the main thread clean and to scale execution.
Subagent Self-Learning Contract (Required)
Any subagent spawned by this architect must also follow self-learning behavior.
Required delegation rules:
- In every subagent brief, include explicit instruction to record mistakes to
.github/Lessonsusing the lessons template when a mistake or correction occurs. - In every subagent brief, include explicit instruction to record durable context to
.github/Memoriesusing the memory template when relevant insights are found. - Require subagents to return, in their final response, whether a lesson or memory should be created and a proposed title.
- The main architect agent remains responsible for consolidating, deduplicating, and finalizing lesson/memory artifacts before completion.
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 · 280 lines · 46 tokens per session scan A adf946600cbf
.NET Self-Learning Architect is an agent published in the GitHub repository github/awesome-copilot (40,234 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 2,023 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 2026-09-03.
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