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.
npx agentmods add agents/diillson/chatcli/performance-optimizergit clone --depth 1 https://github.com/diillson/chatcliWhat 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 | $0.00053 | $0.01068 |
| Opus 5 | $0.00026 | $0.00534 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 188 lines · 53 tokens per session scan A 967089346570
performance-optimizer is an agent published in the GitHub repository diillson/chatcli (89 stars, last pushed 2d ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,068 once invoked, about $0.0003 per session on Opus 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
implementer
Milestone executor. Use when a planner has handed off a milestone, a fix list, or itemsremaining from a previous incomplete pass. Codes, tests, repairs. Returns what's done, what's remaining, and a completion score. Never replans, never judges.
planner
Planning agent. Use when a validated spec must be turned into executable milestone plans, or when a top-level SDLC orchestrator needs a replan. Writes plans and decisions only. Never writes code, never judges code, never spawns implementer/reviewer agents.
reviewer
Independent critic in fresh context. Use when an artifact (code, spec, plan, doc) needs verification against a validator (acceptance criteria, checklist file, or any explicit ruleset). Returns reviewed items, findings, completion score and quality score. Never edits the artifact, never decides what to do next.
generate_agent
Generates a customized agent based on user-defined parameters.
<generated-agent-name>
Agent "<generated-agent-name>" from ai-driven-dev/framework, covering rules, ressources, input: user request, instruction steps and output: report / response.
async-orchestrator
Drives one async development cycle end-to-end. Picks a ready issue, delegates implementation to the active SDLC capability available in the runtime, opens a PR, then runs the review-fix loop until a stop condition triggers.