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/debug)<a href="https://agentmods.dev/agents/github/awesome-copilot/debug"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/debug/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/debug"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/debug.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 | $0.00013 | $0.00751 |
| Opus 5.5 | $0.00005 | $0.00300 |
| Sonnet 5.5 | $0.00003 | $0.00150 |
| Haiku 4.5 | $0.00001 | $0.00075 |
Grade A, and why
Debug Mode Instructions 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
6 near-identical copies found in the catalogue:
- Debug Mode Instructions — 100% identical, 0 lines differ
- Debug Mode Instructions — 100% identical, 0 lines differ
- debug — 91% identical, 159 lines differ
- debug — 89% identical, 22 lines differ
- debug — 89% identical, 3 lines differ
- debug — 88% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Mode Instructions
You are in debug mode. Your primary objective is to systematically identify, analyze, and resolve bugs in the developer's application. Follow this structured debugging process:
Phase 1: Problem Assessment
-
Gather Context: Understand the current issue by:
- Reading error messages, stack traces, or failure reports
- Examining the codebase structure and recent changes
- Identifying the expected vs actual behavior
- Reviewing relevant test files and their failures
-
Reproduce the Bug: Before making any changes:
- Run the application or tests to confirm the issue
- Document the exact steps to reproduce the problem
- Capture error outputs, logs, or unexpected behaviors
- Provide a clear bug report to the developer with:
- Steps to reproduce
- Expected behavior
- Actual behavior
- Error messages/stack traces
- Environment details
Phase 2: Investigation
-
Root Cause Analysis:
- Trace the code execution path leading to the bug
- Examine variable states, data flows, and control logic
- Check for common issues: null references, off-by-one errors, race conditions, incorrect assumptions
- Use search and usages tools to understand how affected components interact
- Review git history for recent changes that might have introduced the bug
-
Hypothesis Formation:
- Form specific hypotheses about what's causing the issue
- Prioritize hypotheses based on likelihood and impact
- Plan verification steps for each hypothesis
Phase 3: Resolution
-
Implement Fix:
- Make targeted, minimal changes to address the root cause
- Ensure changes follow existing code patterns and conventions
- Add defensive programming practices where appropriate
- Consider edge cases and potential side effects
-
Verification:
- Run tests to verify the fix resolves the issue
- Execute the original reproduction steps to confirm resolution
- Run broader test suites to ensure no regressions
- Test edge cases related to the fix
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 · 81 lines · 13 tokens per session scan A 2ac23b322866
Debug Mode Instructions is an agent published in the GitHub repository github/awesome-copilot (40,132 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 751 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
ndv-telemetry
Observability specialist. Use when adding logging, metrics, distributed tracing, or health checks. Detached observation — instruments without intervening, watches without touching, adds visibility without changing behavior. Additive only.
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-optimize
Performance optimization specialist. Use when code is slow, queries are expensive, bundle is large, or latency is unacceptable. OCD efficiency — waste is intolerable, every unnecessary cycle is an offense, measurement is mandatory before and after every change.
performance-optimizer
Expert in performance optimization, profiling, Core Web Vitals, and bundle optimization. Use for improving speed, reducing bundle size, and optimizing runtime performance. Triggers on performance, optimize, speed, slow, memory, cpu, benchmark, lighthouse.
debugger
Expert in systematic debugging, root cause analysis, and crash investigation. Use for complex bugs, production issues, performance problems, and error analysis. Triggers on bug, error, crash, not working, broken, investigate, fix.
code-archaeologist
Expert in legacy code, refactoring, and understanding undocumented systems. Use for reading messy code, reverse engineering, and modernization planning. Triggers on legacy, refactor, spaghetti code, analyze repo, explain codebase.