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 oyi77/1ai-skills --skill skill-performance-monitorgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/skill-performance-monitor)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/skill-performance-monitor"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/skill-performance-monitor/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/oyi77/1ai-skills/skill-performance-monitor"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/skill-performance-monitor.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 | $0.00047 | $0.02593 |
| Opus 5.5 | $0.00019 | $0.01037 |
| Sonnet 5.5 | $0.00009 | $0.00519 |
| Haiku 4.5 | $0.00005 | $0.00259 |
Grade A, and why
skill-performance-monitor 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.
How it starts
The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
persona: name: "Brendan Gregg" title: "The Performance Engineering Expert - Master of Metrics" expertise: ['Performance Monitoring', 'Metrics', 'Observability', 'Analytics'] philosophy: "If you can't measure it, you can't improve it." credentials: ['Netflix Senior Performance Engineer', "Author of 'Systems Performance'", 'Created performance methodologies'] principles: ['Measure what matters', 'Alert on symptoms', 'Analyze root causes', 'Optimize iteratively']
Skill Performance Monitor
Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising. |
| "I already know this topic" | Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps. |
| "This doesn't apply to my situation" | The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold. |
| "One more tool will fix it" | Adding complexity rarely solves process gaps. Master the core workflow first. |
When to Use
Trigger phrases:
- "skill performance monitor"
- "Help me with skill performance monitor"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
Overview
Monitor skill performance in real-time. Track usage patterns, success rates, response quality, and user satisfaction. Use data-driven insights to optimize skill selection and improve overall system effectiveness.
Purpose: Real-time skill analytics
Target: All 1ai-skills
Output: Optimization recommendations
Core Functions
- Primary operation execution with input validation
- Error detection and automatic recovery
- Output formatting and quality assurance
- Integration hooks for downstream consumers
1. Usage Tracking
Track:
- How often each skill is used
- When skills are selected
- Success/failure of skill tasks
- Time to complete tasks
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 First seen · 453 lines · 47 tokens per session scan A 20a20948b01c
skill-performance-monitor is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 2,593 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 2026-09-04.
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