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 thienanblog/awesome-ai-agent-skills --skill performance-optimizationgit clone --depth 1 https://github.com/thienanblog/awesome-ai-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/thienanblog/awesome-ai-agent-skills/performance-optimization)<a href="https://agentmods.dev/skills/thienanblog/awesome-ai-agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/performance-optimization/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/thienanblog/awesome-ai-agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/performance-optimization.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 | $0.00036 | $0.00769 |
| Opus 5.5 | $0.00014 | $0.00308 |
| Sonnet 5.5 | $0.00007 | $0.00154 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
performance-optimization 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 27d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Make a performance claim that survives scrutiny: a confirmed bottleneck, a change that addresses it, and a before/after comparison under the same conditions, with business behavior unchanged.
Delegation
Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.
Define the claim and its envelope
Establish what is slow, where, for whom, and compared to what, then capture a baseline before changing anything: timing, query count, payload, memory, CPU, bundle size, Web Vitals, a profile, or logs.
Results are comparable only inside a fixed benchmark envelope, so record it: workload and data size, starting state, warm or cold cache, account and permissions, command and flags, worker count, retries, resource limits, and concurrent activity on the host. For noisy measurements, repeat enough to report a representative value and its spread; the best run is not the result.
Infrastructure health is part of the measurement. A run with crashes, OOM kills, unexpected restarts, failed setup or cleanup, or orphan processes is rejected and noted, not averaged in.
An unexplained correctness failure comes first: isolate it before optimizing, because a faster wrong answer is not an improvement.
Find the bottleneck
Attribute the time before choosing a fix. Separate backend, database, network, frontend rendering, asset loading, build tooling, and external dependencies; within a test or benchmark run, separate setup, exercise, and cleanup.
Measure the work a flow produces instead of inferring it from where the driver runs. A browser or test runner on the host can still drive memory, CPU, and database load inside services, and a few visible actions can amplify into many requests, statements, rows, jobs, and retries.
Use existing instrumentation, traces, query logs, and profilers, and read the rules that govern the measured path before changing its data flow. performance-playbook.md has domain checks for databases, backends, frontends, assets, builds, tests, and caching.
What ships with it
2 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.
- 27d ago Changed · -41 lines e6c22a3d0d2e
- 1mo ago Changed · -3 lines · -37 tokens per session 3bd9ee78d8dd
- 1mo ago First seen · 94 lines · 73 tokens per session scan A e000163c3b0c
performance-optimization is a skill published in the GitHub repository thienanblog/awesome-ai-agent-skills (70 stars, last pushed 27d ago), licensed Apache-2.0. It adds 36 tokens to every session and 769 once invoked, about $0.0001 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 2026-08-30.
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