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/rileyhilliard/agentrc/optimizing-performancenpx skills add rileyhilliard/agentrc --skill optimizing-performancegit clone --depth 1 https://github.com/rileyhilliard/agentrcWhat 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 | $0.00029 | $0.00772 |
| Opus 5 | $0.00015 | $0.00386 |
| Sonnet 5 | $0.00006 | $0.00154 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
optimizing-performance 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimizing Performance
Core principle: Readable code that's "fast enough" beats complex code that's "optimal". Measure first.
The Golden Rule
IF optimization reduces complexity AND improves performance → ALWAYS DO IT
IF optimization increases complexity → Only if 10x faster OR fixes critical UX (>16ms UI, >100ms input)
Four-Phase Process
- [ ] Phase 1: Measure baseline (time/renders/memory/KB)
- [ ] Phase 2: Identify root cause (algorithm/I/O/payload)
- [ ] Phase 3: Evaluate cost vs benefit
- [ ] Phase 4: Implement & verify improvement
Phase 1: Measure First (REQUIRED)
Never optimize without data.
| Metric | What to Count | Tools |
|---|---|---|
| Time | ms per operation | performance.now(), profilers |
| Re-renders | Component render count | React DevTools Profiler |
| Memory | MB allocated | DevTools Memory tab |
| Network | Request count, KB | Network tab, bundle analyzer |
| Database | Query count, rows scanned | EXPLAIN plans |
Phase 2: Identify Root Cause
| Issue | Indicators | Fix Direction |
|---|---|---|
| O(n²) complexity | Nested loops, .includes() in loop |
Use Set/Map |
| Unnecessary work | Re-computing same result | Cache/memoize |
| I/O bottleneck | N+1 queries, sequential APIs | Batch, use joins |
| Large datasets | Rendering 1000+ items | Virtualization |
| Payload size | >500KB bundles | Tree-shake, lazy load |
Phase 3: Evaluate Cost vs Benefit
- Reduces complexity? → Always do it
- Increases complexity? → Only if 10x faster OR fixes critical UX
- Otherwise → Don't do it
Phase 4: Implement & Verify
- Make minimal changes targeting bottleneck
- Re-run benchmark
- Verify tests pass
Win-Win Optimizations (Always Do)
Multiple loops → Single loop:
// ❌ Three passes
const ids = users.map(u => u.id);
const active = users.filter(u => u.active);
// ✅ One pass
const { ids, active } = users.reduce((acc, u) => {
acc.ids.push(u.id);
if (u.active) acc.active.push(u);
return acc;
}, { ids: [], active: [] });
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 · 101 lines · 29 tokens per session scan A 68f028ca1bfd
optimizing-performance is a skill published in the GitHub repository rileyhilliard/agentrc (3 stars, last pushed 6mo ago), licensed MIT. It adds 29 tokens to every session and 772 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.
Other skills, from other repositories
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brainstorming
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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