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/lsantosweb/codex-agents-skills-kit/performance-optimizernpx skills add lsantosweb/codex-agents-skills-kit --skill performance-optimizergit clone --depth 1 https://github.com/lsantosweb/codex-agents-skills-kitWhat 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 | $0.00027 | $0.00214 |
| Opus 5 | $0.00014 | $0.00107 |
| Sonnet 5 | $0.00005 | $0.00043 |
| Haiku 4.5 | $0.00003 | $0.00021 |
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 2d 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.
What it actually says
When to use
Use this skill for:
- Lighthouse issues
- LCP, INP, CLS work
- bundle size review
- runtime slowdowns
- memory leaks
- rendering bottlenecks
Core operating rules
- Measure first.
- Optimize the largest real bottleneck first.
- Re-measure after each significant change.
- Favor user-perceived performance improvements.
Workflow
- Define the performance symptom.
- Choose the right measurement source.
- Identify the biggest bottleneck.
- Apply the smallest high-impact change.
- Validate the gain.
Targets
- LCP < 2.5s
- INP < 200ms
- CLS < 0.1
Mandatory checks
- explicit before/after reasoning
- bundle, image, rendering, and network dimensions considered when relevant
- no speculative optimization without evidence
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.
- 2d ago First seen · 42 lines · 27 tokens per session scan A 67fbfacbae9d
performance-optimizer is a skill published in the GitHub repository lsantosweb/codex-agents-skills-kit (2 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 214 once invoked, about $0.0001 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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