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/devinilabs/pro-skill/performance-profilingnpx skills add devinilabs/pro-skill --skill performance-profilinggit clone --depth 1 https://github.com/devinilabs/pro-skillWhat 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 | $0.00069 | $0.01287 |
| Opus 5 | $0.00034 | $0.00643 |
| Sonnet 5 | $0.00014 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade A, and why
performance-profiling 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.
This is a copy
100% identical to performance-profiling — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Profiling
Use this skill to diagnose Apple app performance issues systematically, pick the right profiling workflow, apply targeted fixes, and verify the change with real measurements.
Decision Tree
Choose the reference file before changing code:
What performance problem are you investigating?
+ App hangs, stutters, dropped frames, slow UI, high CPU
-> Read references/time-profiler.md
+ High memory, leaks, OOM crashes, growing footprint
-> Read references/memory-profiling.md
+ Slow cold launch, warm launch, resume, or time to first frame
-> Read references/launch-optimization.md
+ Battery drain, thermal throttling, background energy, network waste
-> Read references/energy-diagnostics.md
+ General "app feels slow"
-> Start with references/time-profiler.md, then references/memory-profiling.md
+ Pre-release performance audit
-> Read all reference files and use the review checklist below
Quick Reference
| Problem | Instrument / Tool | Key Metric | Reference |
|---|---|---|---|
| UI hangs over 250 ms | Time Profiler + Hangs | Hang duration, main thread stack | references/time-profiler.md |
| High CPU usage | Time Profiler | CPU percent by function, call tree weight | references/time-profiler.md |
| Memory leak | Leaks + Memory Graph | Leaked bytes, retain cycle paths | references/memory-profiling.md |
| Memory growth | Allocations | Live bytes, generation analysis | references/memory-profiling.md |
| Slow launch | App Launch | Time to first frame, pre-main, post-main | references/launch-optimization.md |
| Battery drain | Energy Log | Energy impact, CPU/GPU/network activity | references/energy-diagnostics.md |
| Thermal issues | Activity Monitor, Instruments | Thermal state transitions | references/energy-diagnostics.md |
| Network waste | Network profiler | Redundant fetches, payload size | references/energy-diagnostics.md |
Workflow
- Identify the performance category from the user report, traces, logs, or code path.
- Read only the matching reference file unless the issue is broad or unclear.
- Prefer real device profiling with a Release build and representative data.
- Inspect the code path named by the profile before proposing a fix.
- Apply the smallest targeted fix that addresses the measured bottleneck.
- Re-profile or add a repeatable measurement to confirm the improvement.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 150 lines · 69 tokens per session scan A e2301598090c
performance-profiling is a skill published in the GitHub repository devinilabs/pro-skill (16 stars, last pushed 19d ago), licensed MIT. It adds 69 tokens to every session and 1,287 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to performance-profiling, differing in 0 lines, and is treated as a copy.
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