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 khasky/awesome-agent-skills --skill awesome-performance-auditgit clone --depth 1 https://github.com/khasky/awesome-agent-skillsWrote 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/khasky/awesome-agent-skills/awesome-performance-audit)<a href="https://agentmods.dev/skills/khasky/awesome-agent-skills/awesome-performance-audit"><img src="https://agentmods.dev/badge/skills/khasky/awesome-agent-skills/awesome-performance-audit/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/khasky/awesome-agent-skills/awesome-performance-audit"><img src="https://agentmods.dev/badge/skills/khasky/awesome-agent-skills/awesome-performance-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.03751 |
| Opus 5.5 | $0.00021 | $0.01500 |
| Sonnet 5.5 | $0.00010 | $0.00750 |
| Haiku 4.5 | $0.00005 | $0.00375 |
Grade A, and why
awesome-performance-audit 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Audit
Audit a server, API, or worker for the runtime and reliability failure modes that cause latency, memory, and throughput problems in production — before micro-optimizing random lines. Treats performance as an operational property with auditable evidence (profiles, traces, code paths, config), not a one-time benchmark. Read-only: it reports findings and a verdict; it never rewrites hot paths. Hand the report to the relevant dev workflow to fix.
Measure, don't guess. Every finding cites its artifact — a profile, a GC trace, a heap delta, a code path, a config value. No profile, no number. A slow-looking loop is a lead; confirm it in a flame graph or trace before flagging.
Six audit tracks, run the ones in scope:
- A. Event-loop discipline (Node.js) — is the loop kept free for short coordination work?
- B. Streaming and backpressure — is unbounded data streamed, or buffered into RAM?
- C. Memory and CPU diagnostics — are the signals watched, and is the workflow repeatable?
- D. Production reliability — timeouts, shutdown, limits, job hygiene.
- E. Resilience and failure paths — circuit breakers, retry budgets, queue topology, cross-service failure containment.
- F. Frontend delivery (web) — Core Web Vitals, bundle weight, hydration and render cost.
Scope and method
- Establish scope — one endpoint, one job class, or the whole service. Name the workload; perf is meaningless without "under what load".
- Gather evidence — CPU profile for hot paths, heap snapshots for growth, GC traces for pressure, request/job correlation to connect symptoms to workloads. Read code paths and config (timeouts, body limits, pool sizes). Keep the raw profiles and traces the audit pulled, named by target and timestamp and outside the repository, so a re-audit can diff against them instead of overwriting them.
- Measure the tail, not the average — p95/p99/max, not mean. Averages hide the requests that actually hurt.
- Score, gate, report — see Output.
Done when: the workload is named, the tail numbers are measured rather than estimated, every track in scope has been walked, and anything that could only be settled under real load is reported as unmeasured.
What ships with it
1 file 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.
- 4d ago Changed fa8cca654052
- 5d ago Changed 09145bfc9919
- 14d ago Changed · +4 lines 7019a2dbd9d3
- 29d ago Changed · -95 tokens per session 30966ae85140
- 1mo ago Changed 0ed3ef12d526
- 1mo ago Changed 588f6874bc1a
- 1mo ago Changed · +2 lines · -68 tokens per session ad151bcedfe6
- 1mo ago First seen · 140 lines · 215 tokens per session scan A 8533e3bdbc66
awesome-performance-audit is a skill published in the GitHub repository khasky/awesome-agent-skills (16 stars, last pushed 5d ago), licensed MIT. It adds 52 tokens to every session and 3,751 once invoked, about $0.0002 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 2026-08-31.
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