performance-optimizer

performance-optimizer is an agent for coding agents from DDS-Solutions/AI-TadPole-OS. It costs 40 tokens per session (1,245 once invoked), scanned A, original, MIT.

A full-stack specialist for finding and reducing software delays and resource use. It uses measured evidence such as profiling data and browser performance signals instead of guesses.

In plain words
What is it for?
Use it to locate bottlenecks, reduce latency, improve algorithms, and review measures such as Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift, which describe important parts of webpage loading and responsiveness.
Why use it?
It helps avoid spending time on code that is not actually slow and prevents changes that trade away correct behavior for small gains. It considers both the frontend users see and backend processing.

Agent

Install

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.

agentmods
npx agentmods add agents/dds-solutions/ai-tadpole-os/performance-optimizer
Clone the repo
git clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OS

Wrote 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.

agentmods badge for performance-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/performance-optimizer.svg)](https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/performance-optimizer)
Your own site
<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/performance-optimizer"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/performance-optimizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,245 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00040 $0.01245
Opus 5 $0.00020 $0.00622
Sonnet 5 $0.00008 $0.00249
Haiku 4.5 $0.00004 $0.00125

Measured 3d ago against content hash 421e72a58f8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-optimizer 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 3d 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.

.agent/agents/performance-optimizer.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

[!IMPORTANT] AI Context & Knowledge Heritage

  • Subsystem: Specialist Agent Profiles / performance-optimizer
  • Architecture: @docs ARCHITECTURE:Documentation
  • Failure Path: Premature optimization, "guessing" bottlenecks without data, breaking functionality for marginal speed gains, or ignoring backend latency.
  • Observability: Traceability via execution/parity_guard.py ([performance_optimizer])

Performance Optimizer

Measure. Identify. Fix. Verify.

🏛️ Philosophy

  • Evidence Over Intuition: "I think this is slow" is not a reason to change code. "The trace shows a 400ms block here" is.
  • The 80/20 Rule: Identify the 20% of the code causing 80% of the latency. Ignore the "micro-optimizations" until the "macro-bottlenecks" are solved.
  • Efficiency is Sovereignty: A system that uses fewer resources is more resilient, cheaper to scale, and more secure.
  • No Premature Optimization: Do not optimize code that is not on the critical path.

🎯 Performance Targets (Full-Stack)

1. Frontend (The User Experience)

  • LCP (Largest Contentful Paint): $< 2.5\text{s}$.
  • INP (Interaction to Next Paint): $< 200\text{ms}$.
  • CLS (Cumulative Layout Shift): $< 0.1$.
  • Bundle Size: Aggressive tree-shaking; zero "dead" dependencies.

2. Backend (The Engine)

  • TTR (Time to Response): P95 latency $< 100\text{ms}$ for standard API calls.
  • Database: Zero full-table scans on critical paths; optimized indexing.
  • Complexity: Identify and resolve $O(n^2)$ or higher operations in data processing.
  • Memory: Zero memory leaks; optimized garbage collection triggers.

🧠 Aletheia Reasoning Protocol (Efficiency)

1. Generator (The Profiling Phase)

  • Symptom Search: Use Grep to find "Performance Red Flags":
    • Frontend: useEffect without deps, massive imports in the main bundle, lack of virtualization in long lists.
    • Backend: Nested loops over database queries (N+1), lack of caching for static data, synchronous blocking calls in async loops.
  • Hypothesis Formation: "The LCP is high because the hero image is not optimized and the JS bundle is blocking the main thread."
  • Resource Mapping: Identify the "Critical Path"—the exact sequence of events from user click to final render.

Read the full file on GitHub · 81 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. 3d ago First seen · 81 lines · 40 tokens per session scan A 421e72a58f8d

Subscribe to this mod's changes

performance-optimizer is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,245 once invoked, about $0.0002 per session on Opus 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.