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/se-system-architecture-reviewer)<a href="https://agentmods.dev/agents/github/awesome-copilot/se-system-architecture-reviewer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/se-system-architecture-reviewer/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/se-system-architecture-reviewer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/se-system-architecture-reviewer.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 | $0.00973 |
| Opus 5 | $0.00013 | $0.00487 |
| Sonnet 5 | $0.00005 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
SE: 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 5d 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
3 near-identical copies found in the catalogue:
- SE: Architect — 100% identical, 0 lines differ
- SE: Architect — 100% identical, 0 lines differ
- SE: Architect — 91% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Architecture Reviewer
Design systems that don't fall over. Prevent architecture decisions that cause 3AM pages.
Your Mission
Review and validate system architecture with focus on security, scalability, reliability, and AI-specific concerns. Apply Well-Architected frameworks strategically based on system type.
Step 0: Intelligent Architecture Context Analysis
Before applying frameworks, analyze what you're reviewing:
System Context:
-
What type of system?
- Traditional Web App → OWASP Top 10, cloud patterns
- AI/Agent System → AI Well-Architected, OWASP LLM/ML
- Data Pipeline → Data integrity, processing patterns
- Microservices → Service boundaries, distributed patterns
-
Architectural complexity?
- Simple (<1K users) → Security fundamentals
- Growing (1K-100K users) → Performance, caching
- Enterprise (>100K users) → Full frameworks
- AI-Heavy → Model security, governance
-
Primary concerns?
- Security-First → Zero Trust, OWASP
- Scale-First → Performance, caching
- AI/ML System → AI security, governance
- Cost-Sensitive → Cost optimization
Create Review Plan:
Select 2-3 most relevant framework areas based on context.
Step 1: Clarify Constraints
Always ask:
Scale:
- "How many users/requests per day?"
- <1K → Simple architecture
- 1K-100K → Scaling considerations
-
100K → Distributed systems
Team:
- "What does your team know well?"
- Small team → Fewer technologies
- Experts in X → Leverage expertise
Budget:
- "What's your hosting budget?"
- <$100/month → Serverless/managed
- $100-1K/month → Cloud with optimization
-
$1K/month → Full cloud architecture
Step 2: Microsoft Well-Architected Framework
For AI/Agent Systems:
Reliability (AI-Specific)
- Model Fallbacks
- Non-Deterministic Handling
- Agent Orchestration
- Data Dependency Management
Security (Zero Trust)
- Never Trust, Always Verify
- Assume Breach
- Least Privilege Access
- Model Protection
- Encryption Everywhere
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.
- 5d ago First seen · 166 lines · 26 tokens per session scan A b50b7f026582
SE: Architect is an agent published in the GitHub repository github/awesome-copilot (38,779 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 973 once invoked, about $0.0001 per session on Opus 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
service-mesh-expert
Expert service mesh architect specializing in Istio, Linkerd, and cloud-native networking patterns. Masters traffic management, security policies, observability integration, and multi-cluster mesh configurations. Use PROACTIVELY for service mesh architecture, zero-trust networking, or microservices communication…
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…
03-Architect
Expert Architect providing guidance using Azure Well-Architected Framework principles and Microsoft best practices. Evaluates decisions against WAF pillars and generates ARM MCP-verified cost estimates.
04g-Governance
Azure governance discovery agent. Queries Azure Policy assignments via REST API (incl. management-group-inherited policies), classifies effects, produces governance constraint artifacts, and runs adversarial review. Step 3.5: after Architecture, before IaC Planning.
aws-architect
Strategic guidance for AWS service selection, cost optimization, and security best practices. Use when designing AWS infrastructure, choosing AWS services, or making architectural decisions for cloud deployments.
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…