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 orlando-japan/claude-code-setting --skill performance-profilinggit clone --depth 1 https://github.com/orlando-japan/claude-code-settingWrote 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/orlando-japan/claude-code-setting/performance-profiling)<a href="https://agentmods.dev/skills/orlando-japan/claude-code-setting/performance-profiling"><img src="https://agentmods.dev/badge/skills/orlando-japan/claude-code-setting/performance-profiling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/orlando-japan/claude-code-setting/performance-profiling"><img src="https://agentmods.dev/badge/skills/orlando-japan/claude-code-setting/performance-profiling.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.00867 |
| Opus 5.5 | $0.00013 | $0.00347 |
| Sonnet 5.5 | $0.00006 | $0.00173 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
performance-profiling scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Request latency:** `curl -w '%{time_total}\n' -o /dev/null -s URL` How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance profiling
Optimization without measurement is just adding complexity. Measure first, always.
Step 0 — Define "slow"
What does the user experience? What's the actual number, and what's the target?
- "Page takes 4 seconds to load" → specific.
- "Feels slow" → useless. Get numbers.
Without a target, you won't know when to stop optimizing.
Step 1 — Measure the baseline
Use the simplest tool that gives real numbers:
- Request latency:
curl -w '%{time_total}\n' -o /dev/null -s URL - Frontend: browser DevTools Network + Performance panels
- Backend: language-native profiler (cProfile, pprof, async_hooks, perf)
- DB:
EXPLAIN ANALYZE, slow query log
Measure:
- Steady state, not first request (JIT, cache warm-up).
- Multiple samples (p50, p95, p99). Average alone hides tail latency.
- Realistic inputs, not empty fixtures.
Write the number down. You'll need it for the "did it help" check.
Step 2 — Find the biggest contributor
Profilers lie by default when used wrong. Sanity-check:
- Flame graphs for CPU — look for the widest bar at the bottom.
- Trace spans for distributed systems — the long spans are the real cost.
- Allocation profile for memory / GC pressure.
- Query log for DBs — most wall time is usually one or two queries.
80/20 rule. The slowest 20% of your code consumes 80% of the time. Find that 20% and ignore the rest.
Step 3 — Form a hypothesis
State out loud: "I think X is slow because Y." If you can't articulate why, you're guessing.
Common hypotheses:
- N+1 queries. Loop that hits the DB per iteration.
- Missing index. Full table scan.
- Sync I/O in async code. Blocking the event loop.
- Over-serialization. Converting a 10MB blob that gets thrown away.
- Bad cache policy. Cache thrash, miss on hot keys, TTL too short.
- Chatty protocols. Ten round trips where one would do.
Step 4 — Change one thing, measure again
- Change one thing. Multiple changes simultaneously = can't attribute.
- Re-measure with the exact same method. Different method, different numbers.
- Compute the delta. p99 before vs p99 after. Not "it feels faster."
- If no improvement, revert. Don't leave speculative optimizations in the code.
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.
- 1mo ago First seen · 96 lines · 32 tokens per session scan A 6c4ea937e58c
performance-profiling is a skill published in the GitHub repository orlando-japan/claude-code-setting (2 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 867 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
performance-engineer
Enforces measure-profile-optimize order, preventing intuition-driven optimization of non-bottlenecks. Now with mathematical depth metrics, optimization ROI calculus, optimization , and verification gates.
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-optimizer
Analyze and optimize code performance, identify bottlenecks, and suggest improvements.
performance-optimization
Use when performance requirements exist, profiling reveals bottlenecks, or Core Web Vitals need improvement. Do NOT use without evidence — premature optimization adds complexity.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-engineer
!cat skills/shared/protocols/ux-protocol.md 2>/dev/null || true !cat .production-grade.yaml 2>/dev/null || echo "No config — using defaults".