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/avmnu-sng/sutra/performance-profilingnpx skills add avmnu-sng/sutra --skill performance-profilinggit clone --depth 1 https://github.com/avmnu-sng/sutraWrote 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/avmnu-sng/sutra/performance-profiling)<a href="https://agentmods.dev/skills/avmnu-sng/sutra/performance-profiling"><img src="https://agentmods.dev/badge/skills/avmnu-sng/sutra/performance-profiling.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.01962 |
| Opus 5 | $0.00016 | $0.00981 |
| Sonnet 5 | $0.00007 | $0.00392 |
| Haiku 4.5 | $0.00003 | $0.00196 |
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 4d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance profiling
Optimize by measurement, not by theory. This is the empirical loop -- the
runtime counterpart to the design-time load modeling in
/sutra:architecture-review. That skill reasons about how a system should
behave under 2x/5x/10x load; this one measures how it actually behaves right
now and closes the gap. Every step here produces a number. An optimization
without a before-number and an after-number is a guess wearing a diff.
Core principle: the bottleneck is almost never where intuition says it is. Programmers are systematically wrong about where their code spends time -- memory allocation, serialization, a chatty inner loop, and lock contention hide in places no one suspects. Measure first, and let the measurement, not the hunch, pick the target.
When to use
- A workload is too slow and you need to make it faster.
- A latency or throughput regression appeared and you need to find and fix it.
- You are asked to cut cost driven by compute time (CPU-seconds, wall-clock).
- You want to add a performance guard so an existing win cannot silently erode.
When not to use
- Correctness or logic bugs -- fix behavior first; a fast wrong answer is still wrong.
- Design-time capacity questions with no code to run yet -- use
/sutra:architecture-reviewto model load before there is anything to profile. - Speculative tuning of code no measurement has flagged as hot (see the anti-patterns).
1. Baseline first -- get a before-number
You cannot claim an improvement without a number to improve on. Before touching any code, establish a reproducible measurement.
- Pick a representative workload and pin it: same input size, same data, same concurrency, same warm/cold state on every run. A moving workload makes every later comparison meaningless.
- Run it several times and record the distribution, not a single sample -- report a stable statistic (median, or p95 for tail-sensitive work), plus the spread. One run is noise.
- Control the environment: same machine, quiesced background load, warmed caches if the real path is warm. Note anything you could not control.
- Write the baseline down (number, units, workload description, date/commit) so the after-number compares against a fixed reference, not a memory.
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.
- 4d ago First seen · 171 lines · 33 tokens per session scan A bb553c2b4467
performance-profiling is a skill published in the GitHub repository avmnu-sng/sutra (2 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,962 once invoked, about $0.0002 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.
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