caddis-plugin: Instructions file for GitHub Copilot

.github/instructions/performance-optimization.instructions.md

caddis-plugin performance-optimization.instructions.md is an instructions file for GitHub Copilot from saajunaid/caddis-plugin. It costs 2,666 tokens per session, scanned A, original, MIT.

A set of guidelines for improving the speed and efficiency of backend services, databases, and user interfaces.

In plain words
What is it for?
It helps review or write backend, database, and frontend code with performance in mind.
Why use it?
It gives coding agents a consistent reference for spotting and addressing common performance problems.

Instructions file for GitHub Copilot

Written for GitHub Copilot: a Copilot instructions file.

This is saajunaid/caddis-plugin's own configuration. It tells GitHub Copilot how to work on caddis-plugin itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything caddis-plugin configures →

Reuse

Borrowing it

Nothing to install: this file belongs to saajunaid/caddis-plugin. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/saajunaid/caddis-plugin/main/.github/instructions/performance-optimization.instructions.md
Clone the repo
git clone --depth 1 https://github.com/saajunaid/caddis-plugin

Made for: GitHub Copilot.

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Per session 2,666 This file is loaded in full into every session.
When invoked 2,666 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.02666 $0.02666
Opus 5.5 $0.01066 $0.01066
Sonnet 5.5 $0.00533 $0.00533
Haiku 4.5 $0.00267 $0.00267

Measured 1mo ago against content hash c8d3b536d722, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

caddis-plugin performance-optimization.instructions.md 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.

.github/instructions/performance-optimization.instructions.md · 383 lines

How it starts

The opening of the file, as written. The whole thing — 383 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Optimization Guidelines

General Principles

  1. Measure first — Profile before optimizing. Use real data and production-like conditions.
  2. Optimize the common case — Focus on hot paths and frequently executed code.
  3. Avoid premature optimization — Write clear, correct code first. Optimize when measurements prove a bottleneck.
  4. Set performance budgets — Define acceptable latency, throughput, and memory targets before starting.
  5. Optimize at the right level — Algorithm > architecture > code micro-optimization.

Backend Performance

Algorithm and Data Structure Selection

  • Choose appropriate data structures: dict/set for O(1) lookups, sorted containers for range queries
  • Prefer O(n log n) or better algorithms; avoid O(n²) in hot paths
  • Use generators and iterators for large data pipelines to avoid loading everything into memory

Caching

from functools import lru_cache

# In-memory cache for expensive, repeated computations
@lru_cache(maxsize=256)
def get_exchange_rate(currency_code: str, date: str) -> float:
    """Cached exchange rate lookup — date string ensures daily invalidation."""
    return fetch_rate_from_service(currency_code, date)
  • Cache at the right granularity (per-request, per-user, global)
  • Always set TTL (time-to-live) — stale data causes subtle bugs
  • Implement cache invalidation on writes
  • Protect against cache stampede with locking or staggered expiry

Async I/O and Concurrency

import asyncio
import aiohttp

async def fetch_all_endpoints(urls: list[str]) -> list[dict]:
    """Fetch multiple endpoints concurrently."""
    async with aiohttp.ClientSession() as session:
        tasks = [session.get(url) for url in urls]
        responses = await asyncio.gather(*tasks, return_exceptions=True)
        results = []
        for resp in responses:
            if isinstance(resp, Exception):
                results.append({"error": str(resp)})
            else:
                results.append(await resp.json())
        return results

Read the full file on GitHub · 383 lines

Changes

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.

  1. 1mo ago First seen · 383 lines · 2,666 tokens per session scan A c8d3b536d722

Subscribe to this mod's changes

caddis-plugin performance-optimization.instructions.md is an instructions file published in the GitHub repository saajunaid/caddis-plugin (0 stars, last pushed today), licensed MIT. It adds 2,666 tokens to every session, about $0.0107 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 2026-08-31.

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