AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill application-performance-performance-optimizationgit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/application-performance-performance-optimization)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/application-performance-performance-optimization"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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/sickn33/agentic-awesome-skills/application-performance-performance-optimization"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/application-performance-performance-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 35 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.02343 |
| Opus 5.5 | $0.00013 | $0.00937 |
| Sonnet 5.5 | $0.00006 | $0.00469 |
| Haiku 4.5 | $0.00003 | $0.00234 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- application-performance-performance-optimization — 100% identical, 6 lines differ
- application-performance-performance-optimization — 100% identical, 0 lines differ
- application-performance-performance-optimization — 97% identical, 8 lines differ
- application-performance-performance-optimization — 97% identical, 8 lines differ
- application-performance-performance-optimization — 97% identical, 8 lines differ
- application-performance-performance-optimization — 92% identical, 13 lines differ
- application-performance-performance-optimization — 92% identical, 13 lines differ
- application-performance-performance-optimization — 92% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 169 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.
- 1mo ago Changed · +6 lines 4d55000f25ad
- 1mo ago First seen · 163 lines · 32 tokens per session scan A 3cb873348afc
application-performance-performance-optimization is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,269 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 2,343 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 2026-09-05.
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