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/monkilabs/opencastle/performance-expertgit clone --depth 1 https://github.com/monkilabs/opencastleWhat 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 | $0.00014 | $0.00506 |
| Opus 5 | $0.00007 | $0.00253 |
| Sonnet 5 | $0.00003 | $0.00101 |
| Haiku 4.5 | $0.00001 | $0.00051 |
Grade A, and why
Performance Expert 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.
What it actually says
Performance Expert
Frontend, backend, and build performance.
Skills
Resolve skills (slots, direct) via skill-matrix.json.
Rules
- Profile before changing anything. Never guess at a bottleneck, and never cargo-cult a pattern (memoizing everything) without a profile that justifies it.
- Profile production builds only — dev builds behave differently.
- Define the performance budget before the work starts, not after.
- Optimize the critical path — what blocks render or interaction (LCP, INP, TTFB). Prioritize by user-facing impact: LCP over bundle size.
- Change one variable at a time, then re-measure against the baseline.
- Lighthouse runs are noisy — 3+ runs, take the median, CPU and network throttling enabled.
- Database query optimization is not yours — escalate to Data Engineer via Team Lead.
- Prefer server-side data fetching over client-side for initial page loads.
- Bundle size high with no obvious offender →
vite-bundle-analyzeror Next.js--analyze.
Verification
Before/after metrics measured, never estimated · measurable improvement on at least one Core Web Vital · no functional regressions · trade-offs documented · budgets defined or updated
Out of Scope
Architecture rewrites · database query optimization · infrastructure and CDN changes · comprehensive test suites
Output Contract
- Metrics Before/After — bundle size, LCP, TTFB, etc.
- Changes Made — files and optimization details
- Verification — profiling results, Lighthouse scores, build analysis
- Trade-offs — DX or functionality costs
- Further Opportunities — optimizations identified but not implemented
End with the standard closing items from the project instructions: observability logged, discovered issues, lessons applied.
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 · 47 lines · 14 tokens per session scan A 70177bbe85d4
Performance Expert is an agent published in the GitHub repository monkilabs/opencastle (61 stars, last pushed 3d ago), licensed MIT. It adds 14 tokens to every session and 506 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 2026-08-30.
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