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 DDS-Solutions/AI-TadPole-OS --skill performance-profilinggit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/dds-solutions/ai-tadpole-os/performance-profiling)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/performance-profiling"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/performance-profiling/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/dds-solutions/ai-tadpole-os/performance-profiling"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/performance-profiling.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 | $0.00032 | $0.01054 |
| Opus 5.5 | $0.00013 | $0.00422 |
| Sonnet 5.5 | $0.00006 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
performance-profiling 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 9d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / performance-profiling
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Premature unmeasured optimization, degraded Core Web Vitals, or undetected main-thread long tasks.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Performance Profiling & Optimization Protocol
Philosophy: Measure, analyze, optimize — in that strict order. Never optimize without a verified baseline.
Workflow Bindings: Integrated into/audit,/enhance, and/ui-ux-pro-max.
🎯 Progressive Disclosure & L3 Reference Index
Read the core Sovereign rules below; consult deep L3 Reference Guides on demand:
| L3 Reference Guide | Purpose | Trigger / When to Load |
|---|---|---|
references/vitals_and_devtools_recipes.md |
Core Web Vitals targets (LCP/INP/CLS), DevTools flame graph analysis, Lighthouse scripts | Performance auditing & bottleneck isolation |
scripts/lighthouse_audit.py |
Automated Lighthouse scoring CLI and JSON reporter with built-in self-tests | Executing headless audits against live URLs |
⚡ 1. The 4-Step Profiling Lifecycle
[1. BASELINE] ───> [2. IDENTIFY] ───> [3. OPTIMIZE] ───> [4. VALIDATE]
Measure current Locate specific Targeted code-split Re-measure to prove
metrics / traces long tasks (>50ms) memoize, or parallel latency gains
📊 2. Core Web Vitals Baseline (Good Targets)
- LCP (Largest Contentful Paint):
< 2.5s(Priority loading, image compression) - INP (Interaction to Next Paint):
< 200ms(Eliminate main-thread blocking tasks) - CLS (Cumulative Layout Shift):
< 0.1(Explicit dimensions on images and dynamic containers)
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.
- 9d ago Changed · +24 lines · +15 tokens per session 96dfcbcee3f6
- 1mo ago First seen · 59 lines · 17 tokens per session scan A 283865d54e29
performance-profiling is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 5d ago), licensed MIT. It adds 32 tokens to every session and 1,054 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 2026-09-03.
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