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/KIMISKI33/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/kimiski33/awesome-copilot/frontend-performance-investigator)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/frontend-performance-investigator"><img src="https://agentmods.dev/badge/agents/kimiski33/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/kimiski33/awesome-copilot/frontend-performance-investigator"><img src="https://agentmods.dev/badge/agents/kimiski33/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 | $0.00017 | $0.00602 |
| Sonnet 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 6d 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.
This is a copy
100% identical to Frontend Performance Investigator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 6d ago First seen · 144 lines · 35 tokens per session scan A fa370a3a79c6
Frontend Performance Investigator is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 4d ago), licensed MIT. It adds 35 tokens to every session and 1,203 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Frontend Performance Investigator, differing in 0 lines, and is treated as a copy.
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