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 skills add T4LEL/Claude-Arsenal --skill optimizegit clone --depth 1 https://github.com/T4LEL/Claude-ArsenalWrote 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/skills/t4lel/claude-arsenal/optimize)<a href="https://agentmods.dev/skills/t4lel/claude-arsenal/optimize"><img src="https://agentmods.dev/badge/skills/t4lel/claude-arsenal/optimize.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00856 |
| Opus 5 | $0.00020 | $0.00428 |
| Sonnet 5 | $0.00008 | $0.00171 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
optimize 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 7d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize
Fast and cheap, with proof — measurement is the entry fee: no measurements, no changes.
Copy this checklist and check off items as you complete them:
Optimize Progress:
- [ ] Step 1: Measure — real numbers across the stack
- [ ] Step 2: Rank — top 1-3 bottlenecks by impact
- [ ] Step 3: Fix — one bottleneck at a time
- [ ] Step 4: Re-measure — before/after pasted, revert if no gain
- [ ] Step 5: Lock in — perf budget recorded in CLAUDE.md
Step 1 — Measure (paste real output, no exceptions)
Run every measurement that applies:
- Lighthouse / Web Vitals on the key pages — home, the page the user called slow, the critical conversion path.
next buildoutput for bundle size and per-route weight (or the equivalent build analyzer for the stack).EXPLAIN ANALYZEon any suspect query.- Supabase MCP
get_advisorsfor index and RLS-performance findings. - The platform cost dashboard (Vercel usage, Supabase billing) for the actual dollar line items.
Numbers recalled from memory or a past session are unverified assumptions — label them that way and re-measure before acting on them. If nothing here can actually be run — no build, no query plan, no dashboard access — stop and report exactly what instrumentation is missing instead of guessing at fixes.
Step 2 — Rank
List the bottlenecks found in Step 1 by user impact (what visitors feel) and cost impact (what shows up on the bill). Pick the top 1-3 only. Fixing bottleneck #7 while #1 still stands is wasted work.
Step 3 — Fix
Delegate to the performance-engineer agent with one bottleneck at a time and the measurement that flagged it. Boring wins first: image formats/sizes, HTTP/data caching, N+1 queries, missing indexes, bundle splitting. Reach for anything exotic only after the boring list is exhausted. If the fix touches a library's caching or data-fetching API — Next.js, the Supabase client, anything version-sensitive — pull current usage via the context7 MCP tools first; these APIs shift across versions and a stale pattern is how "optimizations" quietly regress. Secrets stay in env vars and are never printed into chat, code, logs, or commits — a fix that needs a key belongs in an env var, not inline.
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.
- 7d ago First seen · 58 lines · 40 tokens per session scan A ecc7827d287e
optimize is a skill published in the GitHub repository T4LEL/Claude-Arsenal (1 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 856 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.
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