Borrowing it
Nothing to install: this file belongs to pplancq/copilot-instructions. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pplancq/copilot-instructions/main/.github/instructions/performance-optimization.instructions.mdgit clone --depth 1 https://github.com/pplancq/copilot-instructionsWrote 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/instructions/pplancq/copilot-instructions/performance-optimization)<a href="https://agentmods.dev/instructions/pplancq/copilot-instructions/performance-optimization"><img src="https://agentmods.dev/badge/instructions/pplancq/copilot-instructions/performance-optimization/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/instructions/pplancq/copilot-instructions/performance-optimization"><img src="https://agentmods.dev/badge/instructions/pplancq/copilot-instructions/performance-optimization.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 | $0.04654 | $0.04654 |
| Opus 5.5 | $0.01862 | $0.01862 |
| Sonnet 5.5 | $0.00931 | $0.00931 |
| Haiku 4.5 | $0.00465 | $0.00465 |
Grade C, and why
copilot-instructions performance-optimization.instructions.md scanned grade C with 1 finding 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- End of Performance Optimization Instructions --> This is a copy
98% identical to custom_github_copilot_agent_builder performance-optimization.instructions.md — 64 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 — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization Best Practices
Introduction
Performance isn't just a buzzword—it's the difference between a product people love and one they abandon. I've seen firsthand how a slow app can frustrate users, rack up cloud bills, and even lose customers. This guide is a living collection of the most effective, real-world performance practices I've used and reviewed, covering frontend, backend, and database layers, as well as advanced topics. Use it as a reference, a checklist, and a source of inspiration for building fast, efficient, and scalable software.
General Principles
- Measure First, Optimize Second: Always profile and measure before optimizing. Use benchmarks, profilers, and monitoring tools to identify real bottlenecks. Guessing is the enemy of performance.
- Pro Tip: Use tools like Chrome DevTools, Lighthouse, New Relic, Datadog, Py-Spy, or your language's built-in profilers.
- Optimize for the Common Case: Focus on optimizing code paths that are most frequently executed. Don't waste time on rare edge cases unless they're critical.
- Avoid Premature Optimization: Write clear, maintainable code first; optimize only when necessary. Premature optimization can make code harder to read and maintain.
- Minimize Resource Usage: Use memory, CPU, network, and disk resources efficiently. Always ask: "Can this be done with less?"
- Prefer Simplicity: Simple algorithms and data structures are often faster and easier to optimize. Don't over-engineer.
- Document Performance Assumptions: Clearly comment on any code that is performance-critical or has non-obvious optimizations. Future maintainers (including you) will thank you.
- Understand the Platform: Know the performance characteristics of your language, framework, and runtime. What's fast in Python may be slow in JavaScript, and vice versa.
- Automate Performance Testing: Integrate performance tests and benchmarks into your CI/CD pipeline. Catch regressions early.
- Set Performance Budgets: Define acceptable limits for load time, memory usage, API latency, etc. Enforce them with automated checks.
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 · 465 lines · 4,654 tokens per session scan C 98b07729908b
copilot-instructions performance-optimization.instructions.md is an instructions file published in the GitHub repository pplancq/copilot-instructions (4 stars, last pushed 8mo ago), licensed MIT. It adds 4,654 tokens to every session, about $0.0186 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). It is 98% identical to custom_github_copilot_agent_builder performance-optimization.instructions.md, differing in 64 lines, and is treated as a copy.
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