Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill providing-performance-optimization-advicegit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/providing-performance-optimization-advice)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/providing-performance-optimization-advice"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/providing-performance-optimization-advice/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/foryourhealth111-pixel/vibe-skills/providing-performance-optimization-advice"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/providing-performance-optimization-advice.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 | $0.00053 | $0.00322 |
| Opus 5 | $0.00026 | $0.00161 |
| Sonnet 5 | $0.00011 | $0.00064 |
| Haiku 4.5 | $0.00005 | $0.00032 |
Grade A, and why
providing-performance-optimization-advice 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 8d 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
Performance Optimization Advisor
Positioning
Treat this skill as an explicit/manual advisor for prioritizing optimization work.
When to Use
Use this skill when:
- Prioritizing known or suspected performance bottlenecks from existing logs, traces, benchmarks, or user reports.
- Get recommendations for improving website loading speed.
- Optimize database query performance.
- Improve API response times.
- Reduce infrastructure costs.
Not For / Boundaries
- Build-to-build regression detection: use
detecting-performance-regressions - Raw benchmark execution or profiling capture: use
performance-testing - Test-result packaging for QA audiences: use
generating-test-reports
Typical Outputs
- A ranked optimization backlog by impact and effort
- Concrete hotspots by layer: frontend, backend, infra, or database
- Suggested validation steps to prove the optimization worked
Related Skills
detecting-performance-regressionsto prove there is a real regressionperformance-testingto collect the measurements behind the advice
What ships with it
6 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.
- 8d ago First seen · 42 lines · 53 tokens per session scan A 8f8cf2436915
providing-performance-optimization-advice is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 11d ago), licensed Apache-2.0. It adds 53 tokens to every session and 322 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
mobile-performance-react-native
React Native performance profiling, optimization, and monitoring - JS/UI thread analysis, re-render prevention, list optimization, image performance, bundle size, startup time, memory leaks, React Compiler, New Architecture benefits.
api-observability-axiom-pino-sentry
Pino logging, Sentry error tracking, Axiom - structured logging with correlation IDs, error boundaries, performance monitoring, alerting.
api-observability-setup-axiom-pino-sentry
Pino, Axiom, Sentry installation - one-time project setup for logging and error tracking with source maps upload.
web-error-handling-error-boundaries
Error boundary patterns, fallback UI, reset/retry, react-error-boundary library, React 19 createRoot error hooks.
chaos-engineering
Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.
kpi-root-cause-analysis
A root-cause analysis process for KPIs that miss their targets, separating lasting problems from one-time events. It uses factor breakdowns and the 5 Whys method, which repeatedly asks why a problem occurred.