Borrowing it
Nothing to install: this file belongs to ai-dynamo/dynamo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ai-dynamo/dynamo/main/.agents/skills/analyze-aiperf-results/SKILL.mdgit clone --depth 1 https://github.com/ai-dynamo/dynamoWrote 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/ai-dynamo/dynamo/analyze-aiperf-results)<a href="https://agentmods.dev/skills/ai-dynamo/dynamo/analyze-aiperf-results"><img src="https://agentmods.dev/badge/skills/ai-dynamo/dynamo/analyze-aiperf-results/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/ai-dynamo/dynamo/analyze-aiperf-results"><img src="https://agentmods.dev/badge/skills/ai-dynamo/dynamo/analyze-aiperf-results.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 | $0.00054 | $0.02351 |
| Opus 5 | $0.00027 | $0.01175 |
| Sonnet 5 | $0.00011 | $0.00470 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
analyze-aiperf-results 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 6d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 6d ago Changed · +19 lines 3ce28a142027
- 11d ago First seen · 157 lines · 54 tokens per session scan A 0b0e9a9cbc62
analyze-aiperf-results is a skill published in the GitHub repository ai-dynamo/dynamo (8,018 stars, last pushed today), with no licence file. It adds 54 tokens to every session and 2,351 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 2026-08-30.
Other skills, from other repositories
neuron-structured-output
Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…
implementing-container-image-minimal-base-with-distroless
Reduce container attack surface by building application images on Google distroless base images that contain only the application runtime with no shell, package manager, or unnecessary OS utilities.
implementing-rbac-hardening-for-kubernetes
Harden Kubernetes Role-Based Access Control by implementing least-privilege policies, auditing role bindings, eliminating cluster-admin sprawl, and integrating external identity providers.
plano-agent-orchestration
Improve multi-agent orchestration in Plano. Use for agent registration, agent listener wiring, and capability-focused agent descriptions for accurate routing.
plano-filter-guardrails
Harden Plano filter chains and guardrails. Use for MCP filter setup, prompt guard responses, and safe filter ordering.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…