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 lucaspmarie-a11y/claude-skills-vault --skill application-performance-performance-optimizationgit clone --depth 1 https://github.com/lucaspmarie-a11y/claude-skills-vaultWrote 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/lucaspmarie-a11y/claude-skills-vault/application-performance-performance-optimization)<a href="https://agentmods.dev/skills/lucaspmarie-a11y/claude-skills-vault/application-performance-performance-optimization"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/application-performance-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/lucaspmarie-a11y/claude-skills-vault/application-performance-performance-optimization"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/application-performance-performance-optimization.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00032 | $0.02263 |
| Opus 5 | $0.00016 | $0.01131 |
| Sonnet 5 | $0.00006 | $0.00453 |
| Haiku 4.5 | $0.00003 | $0.00226 |
Grade A, and why
application-performance-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 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.
This is a copy
92% identical to application-performance-performance-optimization — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize application performance end-to-end using specialized performance and optimization agents:
[Extended thinking: This workflow orchestrates a comprehensive performance optimization process across the entire application stack. Starting with deep profiling and baseline establishment, the workflow progresses through targeted optimizations in each system layer, validates improvements through load testing, and establishes continuous monitoring for sustained performance. Each phase builds on insights from previous phases, creating a data-driven optimization strategy that addresses real bottlenecks rather than theoretical improvements. The workflow emphasizes modern observability practices, user-centric performance metrics, and cost-effective optimization strategies.]
Use this skill when
- Coordinating performance optimization across backend, frontend, and infrastructure
- Establishing baselines and profiling to identify bottlenecks
- Designing load tests, performance budgets, or capacity plans
- Building observability for performance and reliability targets
Do not use this skill when
- The task is a small localized fix with no broader performance goals
- There is no access to metrics, tracing, or profiling data
- The request is unrelated to performance or scalability
Instructions
- Confirm performance goals, constraints, and target metrics.
- Establish baselines with profiling, tracing, and real-user data.
- Execute phased optimizations across the stack with measurable impact.
- Validate improvements and set guardrails to prevent regressions.
Safety
- Avoid load testing production without approvals and safeguards.
- Roll out performance changes gradually with rollback plans.
Phase 1: Performance Profiling & Baseline
1. Comprehensive Performance Profiling
- Use Task tool with subagent_type="performance-engineer"
- Prompt: "Profile application performance comprehensively for: $ARGUMENTS. Generate flame graphs for CPU usage, heap dumps for memory analysis, trace I/O operations, and identify hot paths. Use APM tools like DataDog or New Relic if available. Include database query profiling, API response times, and frontend rendering metrics. Establish performance baselines for all critical user journeys."
- Context: Initial performance investigation
- Output: Detailed performance profile with flame graphs, memory analysis, bottleneck identification, baseline metrics
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 · 158 lines · 32 tokens per session scan A 9c0f62c3c079
application-performance-performance-optimization is a skill published in the GitHub repository lucaspmarie-a11y/claude-skills-vault (5 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 2,263 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to application-performance-performance-optimization, differing in 13 lines, and is treated as a copy.
Other skills, from other repositories
gke-compute-classes
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
cloud-architect
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost…