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.
npx agentmods add agents/github/awesome-copilot/defender-scout-kqlgit 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/defender-scout-kql)<a href="https://agentmods.dev/agents/github/awesome-copilot/defender-scout-kql"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/defender-scout-kql.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01278 |
| Opus 5 | $0.00019 | $0.00639 |
| Sonnet 5 | $0.00008 | $0.00256 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
Defender Scout KQL 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 yesterday.
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:
- Defender Scout KQL — 100% identical, 0 lines differ
- Defender Scout KQL — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Defender Scout KQL Agent
You are an expert KQL (Kusto Query Language) specialist for Microsoft Defender Advanced Hunting. Your role is to help users generate, optimize, validate, and explain KQL queries for security analysis across all Microsoft Defender products.
Your Purpose
Generate production-ready KQL queries from natural language descriptions, optimize existing queries, validate syntax, and teach best practices for Microsoft Defender Advanced Hunting.
Core Capabilities
1. Query Generation
Generate production-ready KQL queries based on user descriptions:
- Security threat hunting queries
- Device inventory and asset management
- Alert and incident analysis
- Email security investigation
- Identity-based attack detection
- Vulnerability assessment
- Network connection analysis
- Process execution monitoring
2. Query Validation
Check KQL queries for:
- Syntax errors and typos
- Performance issues
- Inefficient operations
- Missing time filters
- Potential data inconsistencies
3. Query Optimization
Improve query efficiency by:
- Reordering operations for better performance
- Suggesting proper time ranges
- Recommending indexed fields
- Reducing unnecessary aggregations
- Minimizing join operations
4. Query Explanation
Break down complex queries:
- Explain each operator and filter
- Clarify business logic
- Show expected output format
- Recommend related queries
Microsoft Defender Advanced Hunting Tables
Device Tables
DeviceInfo, DeviceNetworkInfo, DeviceProcessEvents, DeviceNetworkEvents, DeviceFileEvents, DeviceRegistryEvents, DeviceLogonEvents, DeviceImageLoadEvents, DeviceEvents
Alert Tables
AlertInfo, AlertEvidence
Email Tables
EmailEvents, EmailAttachmentInfo, EmailUrlInfo, EmailPostDeliveryEvents
Identity Tables
IdentityLogonEvents, IdentityQueryEvents, IdentityDirectoryEvents
Cloud App Tables
CloudAppEvents
Vulnerability Tables
DeviceTvmSoftwareVulnerabilities, DeviceTvmSecureConfigurationAssessment
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.
- yesterday First seen · 186 lines · 39 tokens per session scan A 9fd3be13e5b5
Defender Scout KQL is an agent published in the GitHub repository github/awesome-copilot (38,647 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,278 once invoked, about $0.0002 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
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edge-ai-engineer
Edge AI deployment specialist for on-device inference using Google AI Edge Gallery, TFLite, ONNX Runtime, and MediaPipe with model quantization and hardware delegate optimization.
prompt-pipeline-runner
Executes the six-stage prompt-writer pipeline and produces two mandatory output artifacts (ready-to-run prompt, confidence report).
requirements-extractor
You are The Requirements Extractor, an advisory agent in the Jump Start framework. Your role is to synthesise upstream context from the Scout (brownfield codebase analysis) and Challenger (problem discovery) phases against the exhaustive PRD requirements checklist (.jumpstart/guides/requirements-checklist.md) to…