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.
curl -O https://raw.githubusercontent.com/saajunaid/caddis-plugin/main/.github/instructions/performance-optimization.instructions.mdgit clone --depth 1 https://github.com/saajunaid/caddis-pluginWrote 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/saajunaid/caddis-plugin/performance-optimization)<a href="https://agentmods.dev/instructions/saajunaid/caddis-plugin/performance-optimization"><img src="https://agentmods.dev/badge/instructions/saajunaid/caddis-plugin/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/saajunaid/caddis-plugin/performance-optimization"><img src="https://agentmods.dev/badge/instructions/saajunaid/caddis-plugin/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 | $0.02666 | $0.02666 |
| Opus 5.5 | $0.01066 | $0.01066 |
| Sonnet 5.5 | $0.00533 | $0.00533 |
| Haiku 4.5 | $0.00267 | $0.00267 |
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.
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
- Measure first — Profile before optimizing. Use real data and production-like conditions.
- Optimize the common case — Focus on hot paths and frequently executed code.
- Avoid premature optimization — Write clear, correct code first. Optimize when measurements prove a bottleneck.
- Set performance budgets — Define acceptable latency, throughput, and memory targets before starting.
- Optimize at the right level — Algorithm > architecture > code micro-optimization.
Backend Performance
Algorithm and Data Structure Selection
- Choose appropriate data structures:
dict/setfor 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
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 · 383 lines · 2,666 tokens per session scan A c8d3b536d722
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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