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 skills/xenitv1/claude-code-maestro/optimization-masterynpx skills add xenitV1/claude-code-maestro --skill optimization-masterygit clone --depth 1 https://github.com/xenitV1/claude-code-maestroWhat 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 | $0.00047 | $0.00788 |
| Opus 5 | $0.00023 | $0.00394 |
| Sonnet 5 | $0.00009 | $0.00158 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
optimization-mastery 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.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<domain_overview>
⚡ OPTIMIZATION MASTERY: THE VELOCITY CORE
Philosophy: Efficiency is the highest form of quality. Minimal overhead, maximum impact. Performance-First is the only law. INTERACTION HYGIENE MANDATE (CRITICAL): Never prioritize synthetic benchmarks over real-world interaction smoothness. AI-generated code often misses Interaction to Next Paint (INP) bottlenecks caused by synchronous main-thread blocking. You MUST use
scheduler.yield()orrequestAnimationFramefor any complex DOM or state updates triggered by user events. Any implementation that risks "Layout Thrashing" or exceeds the 200ms INP threshold must be rejected. </domain_overview> <frontend_velocity>
🎨 PROTOCOL 1: FRONTEND PRECISION (INP & BUNDLE)
Aesthetics must be fast. Refer to frontend-design for visuals, but enforce these for speed.
- The INP Threshold:
- Core Metric: Interaction to Next Paint (INP) MUST be < 200ms.
- Action: Yield to main thread for heavy logic. Use
scheduler.yield()orrequestIdleCallback.
- Hydration Strategies:
- Mandatory: Use Partial Hydration or Resumability (e.g. Qwik/Astro patterns).
- Forbidden: Massive "Full Hydration" of static content.
- Asset Governance:
- Images: Modern formats (AVIF/WebP) with
srcsetare mandatory. - Fonts: Only
wghtvariable fonts; subsetted. </frontend_velocity> <backend_velocity>
- Images: Modern formats (AVIF/WebP) with
🏗️ PROTOCOL 2: BACKEND VELOCITY (QUERY & DATA)
The backend must be a fortress of speed. Refer to backend-design for architecture.
- Identifier Strategy:
- Mandatory: Use UUIDv7 for all primary keys in high-insert tables.
- Rationale: Time-sortable IDs prevent B-tree fragmentation and boost insert speed by ~30%.
- Query Budget:
- Max Latency: Sub-100ms for OLTP queries.
- Action: Every index MUST be a "Covering Index" for critical read paths.
- Edge compute:
- Offload logic to Edge Functions (Vercel/Cloudflare) to reduce Time-to-First-Byte (TTFB). </backend_velocity> <ai_token_stewardship>
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 · 53 lines · 47 tokens per session scan A 56c0d8cbef65
optimization-mastery is a skill published in the GitHub repository xenitV1/claude-code-maestro (230 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 788 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 2026-08-30.
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