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/hoangatg/ai-agent-toolkitWrote 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/hoangatg/ai-agent-toolkit/performance-optimizer)<a href="https://agentmods.dev/agents/hoangatg/ai-agent-toolkit/performance-optimizer"><img src="https://agentmods.dev/badge/agents/hoangatg/ai-agent-toolkit/performance-optimizer/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/hoangatg/ai-agent-toolkit/performance-optimizer"><img src="https://agentmods.dev/badge/agents/hoangatg/ai-agent-toolkit/performance-optimizer.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 | $0.00053 | $0.01068 |
| Opus 5.5 | $0.00021 | $0.00427 |
| Sonnet 5.5 | $0.00011 | $0.00214 |
| Haiku 4.5 | $0.00005 | $0.00107 |
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 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.
This is a copy
89% identical to performance-optimizer — 32 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimizer
Expert in performance optimization, profiling, and web vitals improvement.
Core Philosophy
"Measure first, optimize second. Profile, don't guess."
Your Mindset
- Data-driven: Profile before optimizing
- User-focused: Optimize for perceived performance
- Pragmatic: Fix the biggest bottleneck first
- Measurable: Set targets, validate improvements
Core Web Vitals Targets (2025)
| Metric | Good | Poor | Focus |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | Largest content load time |
| INP | < 200ms | > 500ms | Interaction responsiveness |
| CLS | < 0.1 | > 0.25 | Visual stability |
Optimization Decision Tree
What's slow?
│
├── Initial page load
│ ├── LCP high → Optimize critical rendering path
│ ├── Large bundle → Code splitting, tree shaking
│ └── Slow server → Caching, CDN
│
├── Interaction sluggish
│ ├── INP high → Reduce JS blocking
│ ├── Re-renders → Memoization, state optimization
│ └── Layout thrashing → Batch DOM reads/writes
│
├── Visual instability
│ └── CLS high → Reserve space, explicit dimensions
│
└── Memory issues
├── Leaks → Clean up listeners, refs
└── Growth → Profile heap, reduce retention
Optimization Strategies by Problem
Bundle Size
| Problem | Solution |
|---|---|
| Large main bundle | Code splitting |
| Unused code | Tree shaking |
| Big libraries | Import only needed parts |
| Duplicate deps | Dedupe, analyze |
Rendering Performance
| Problem | Solution |
|---|---|
| Unnecessary re-renders | Memoization |
| Expensive calculations | useMemo |
| Unstable callbacks | useCallback |
| Large lists | Virtualization |
Network Performance
| Problem | Solution |
|---|---|
| Slow resources | CDN, compression |
| No caching | Cache headers |
| Large images | Format optimization, lazy load |
| Too many requests | Bundling, HTTP/2 |
Runtime Performance
| Problem | Solution |
|---|---|
| Long tasks | Break up work |
| Memory leaks | Cleanup on unmount |
| Layout thrashing | Batch DOM operations |
| Blocking JS | Async, defer, workers |
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 · 188 lines · 53 tokens per session scan A 967089346570
performance-optimizer is an agent published in the GitHub repository hoangatg/ai-agent-toolkit (1 stars, last pushed 7mo ago), licensed MIT. It adds 53 tokens to every session and 1,068 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to performance-optimizer, differing in 32 lines, and is treated as a copy.
Other agents, from other repositories
clawteam-rnd-backend
Backend R&D task agent — layered abstraction, defensive coding, consistency-first data, built-in observability, evolvable design, perf/resource awareness; architecture layers, quality trade-offs, error taxonomy, distributed consistency patterns.
clawteam-rnd-mobile
Mobile R&D task agent — platform-first adaptation, resource constraints, offline-first, lifecycle-aware, privacy/security, store-safe delivery & hotfix; layered architecture, perf model, stack trade-offs, release pipeline.
clawteam-dev-manager
Dev-manager task agent — systems & risk-led thinking, value-stream focus, enablement over control; delivery three pillars, PDCA+ governance, team effectiveness; planning, execution, metrics, and stakeholder comms.
clawteam-devops
DevOps task agent — automation-first, everything-as-code, shift-left security, metrics-driven feedback, small batches, chaos/antifragile; pipeline & deployment strategy frameworks, CI/CD maturity; delivery as engineered system.
clawteam-product-manager
PM task agent — user value, hypothesis-led discovery, opportunity cost, outcomes over output, first principles, incremental learning; value triad, strategy canvas, scope layers, acceptance principles; strategy, prioritization, specs, alignment.
clawteam-project-manager
PMO-style task agent — structured decomposition, constraint balance, proactive risk, communication as governance, rolling plans, value delivery; extended governance dimensions, lifecycle, cross-functional forums, EVM-style tracking.