Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/nguyenthienthanh/aura-frognpx agentmods add skills/nguyenthienthanh/aura-frog/perf-profilingWrote 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/nguyenthienthanh/aura-frog/perf-profiling)<a href="https://agentmods.dev/skills/nguyenthienthanh/aura-frog/perf-profiling"><img src="https://agentmods.dev/badge/skills/nguyenthienthanh/aura-frog/perf-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/nguyenthienthanh/aura-frog/perf-profiling"><img src="https://agentmods.dev/badge/skills/nguyenthienthanh/aura-frog/perf-profiling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 157 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
- medium Agent Snooping · line 217 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 218 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00107 | $0.02185 |
| Opus 5.5 | $0.00043 | $0.00874 |
| Sonnet 5.5 | $0.00021 | $0.00437 |
| Haiku 4.5 | $0.00011 | $0.00218 |
Grade A, and why
perf-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 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.
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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-consumed reference. Optimized for Claude to read during execution. Human-readable explanation: see docs/architecture/HIERARCHICAL_PLANNING.md or docs/getting-started/ depending on topic.
Performance Profiling
Rule 0: Don't optimize what you haven't measured. Almost all "obvious" optimizations are wrong.
The Protocol
Step 1 — Define "fast enough"
Performance is relative. Before optimizing:
- Current metric (p50, p95, p99 latency / MB memory / % CPU)
- Target metric (what's the SLO/SLA/user expectation?)
- Gap — what needs to close
If no target → ask the user. Don't optimize without a goal — you'll polish forever.
Step 2 — Measure baseline
Use the appropriate profiler:
| Language | Profiler |
|---|---|
| Node.js | node --prof, clinic.js, 0x |
| Browser JS | Chrome DevTools Performance tab |
| Python | cProfile, py-spy, memory_profiler |
| Go | pprof (built-in) |
| Rust | cargo flamegraph, perf |
| CLI | hyperfine |
| HTTP | wrk, k6, vegeta |
Run the profiler under realistic load, not trivial input. Capture for 30–60s minimum — short captures miss long-tail events.
Step 3 — Analyze (flamegraph / top consumers)
Identify the top 3 functions by:
- Self time (function's own CPU, excluding descendants)
- Total time (self + descendants)
- Call count (high-frequency cold functions add up)
Pareto check: Is there a clear 80/20?
- Top function takes 50%+ of time → that's your target
- Time spread evenly across 100 functions → hard to optimize; likely needs architectural change
Step 4 — Form optimization hypothesis
For the bottleneck:
- What's it doing? (read the code)
- Why is it slow? (algorithm, I/O, allocations, lock contention, cache miss)
- What's a realistic improvement target?
Common bottleneck classes + fixes:
| Bottleneck | Fix |
|---|---|
| O(n²) on large n | Change to O(n log n) or O(n) |
| Sync I/O in hot path | Async, batch, or remove the I/O |
| Allocations in loop | Pre-allocate, object pool, reuse |
| Redundant computation | Memoize, cache result |
| Lock contention | Lock-free structure, sharding, immutable data |
| Large object serialization | Streaming, lazy fields, columnar format |
| Database N+1 | Join, prefetch, dataloader pattern |
| Cold cache / page fault | Warm up, prefetch, locality |
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 · 222 lines · 107 tokens per session scan A 69ff465711d0
perf-profiling is a skill published in the GitHub repository nguyenthienthanh/aura-frog (25 stars, last pushed 3d ago), licensed MIT. It adds 107 tokens to every session and 2,185 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
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