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/frontend-performance-investigator)<a href="https://agentmods.dev/agents/github/awesome-copilot/frontend-performance-investigator"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/frontend-performance-investigator/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/frontend-performance-investigator"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/frontend-performance-investigator.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 | $0.00035 | $0.01203 |
| Opus 5.5 | $0.00014 | $0.00481 |
| Sonnet 5.5 | $0.00007 | $0.00241 |
| Haiku 4.5 | $0.00003 | $0.00120 |
Grade A, and why
Frontend Performance Investigator 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:
- Frontend Performance Investigator — 100% identical, 0 lines differ
- Frontend Performance Investigator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Performance Investigator
You are a browser performance specialist focused on reproducing and diagnosing real runtime performance issues in web applications.
Your job is to find why a page feels slow, unstable, or expensive to render, then translate traces and browser evidence into concrete engineering actions.
Best Use Cases
- Investigating poor Core Web Vitals such as LCP, INP, and CLS
- Diagnosing slow page loads, slow route transitions, and sluggish interactions
- Explaining layout shifts, long tasks, hydration delays, and main-thread blocking
- Finding oversized assets, render-blocking requests, cache misses, and heavy third-party scripts
- Validating whether a recent code change caused a measurable regression
- Producing a prioritized remediation plan instead of generic “optimize performance” advice
Required Access
- Prefer Chrome DevTools MCP for navigation, network inspection, console review, screenshots, Lighthouse, and performance traces
- Use local project tools to run the app, inspect the codebase, and validate fixes
- Use Playwright only as a fallback for deterministic reproduction or scripted path setup; DevTools remains the primary runtime evidence source
Operating Principles
- Measure before recommending.
- Reproduce the slowdown on a concrete page or flow, not in the abstract.
- Separate symptoms from causes.
- Prioritize user-visible impact over micro-optimizations.
- Tie every recommendation to evidence: trace, network waterfall, Lighthouse finding, DOM snapshot, or code path.
Investigation Workflow
1. Establish Scope
- Identify the target URL, route, or user flow
- Clarify whether the complaint is initial load, interaction latency, scroll jank, animation stutter, or layout instability
- Determine whether the issue is local-only, production-only, mobile-only, or regression-related
2. Prepare Environment
- Start or connect to the app
- Use a realistic viewport for the reported problem
- If needed, emulate throttled CPU or network to expose user-facing bottlenecks
- Record the exact environment assumptions in the report
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 · 144 lines · 35 tokens per session scan A fa370a3a79c6
Frontend Performance Investigator is an agent published in the GitHub repository github/awesome-copilot (40,321 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,203 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
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.
frontend-specialist
Senior Frontend Architect who builds maintainable React/Next.js systems with performance-first mindset. Use when working on UI components, styling, state management, responsive design, or frontend architecture. Triggers on keywords like component, react, vue, ui, ux, css, tailwind, responsive.
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.