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/steipete/agent-scripts/native-app-performancenpx skills add steipete/agent-scripts --skill native-app-performancegit clone --depth 1 https://github.com/steipete/agent-scriptsWhat 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 | $0.00020 | $0.00502 |
| Opus 5 | $0.00010 | $0.00251 |
| Sonnet 5 | $0.00004 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
native-app-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.
What it actually says
Native App Performance (CLI-only)
Goal: record Time Profiler via xctrace, extract samples, symbolicate, and propose hotspots without opening Instruments.
Quick start (CLI)
- Record Time Profiler (attach):
# Start app yourself, then attach
xcrun xctrace record --template 'Time Profiler' --time-limit 90s --output /tmp/App.trace --attach <pid>
- Record Time Profiler (launch):
xcrun xctrace record --template 'Time Profiler' --time-limit 90s --output /tmp/App.trace --launch -- /path/App.app/Contents/MacOS/App
- Extract time samples:
scripts/extract_time_samples.py --trace /tmp/App.trace --output /tmp/time-sample.xml
- Get load address for symbolication:
# While app is running
vmmap <pid> | rg -m1 "__TEXT" -n
- Symbolicate + rank hotspots:
scripts/top_hotspots.py --samples /tmp/time-sample.xml \
--binary /path/App.app/Contents/MacOS/App \
--load-address 0x100000000 --top 30
Workflow notes
- Always confirm you’re profiling the correct binary (local build vs /Applications). Prefer direct binary path for
--launch. - Ensure you trigger the slow path during capture (menu open/close, refresh, etc.).
- If stacks are empty, capture longer or avoid idle sections.
xcrun xctrace help recordandxcrun xctrace help exportshow correct flags.
Included scripts
scripts/record_time_profiler.sh: record via attach or launch.scripts/extract_time_samples.py: export time-sample XML from a trace.scripts/top_hotspots.py: symbolicate and rank top app frames.
Gotchas
- ASLR means you must use the runtime
__TEXTload address fromvmmap. - If using a new build, update the
--binarypath; symbols must match the trace. - CLI-only flow: no need to open Instruments if stacks are symbolicated via
atos.
What ships with it
3 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 · 64 lines · 20 tokens per session scan A 0c8a67d976b2
native-app-performance is a skill published in the GitHub repository steipete/agent-scripts (6,580 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 502 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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