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 Leeroo-AI/leeroopedia-mcp --skill ml_inference_optimizationgit clone --depth 1 https://github.com/Leeroo-AI/leeroopedia-mcpWrote 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/leeroo-ai/leeroopedia-mcp/ml_inference_optimization)<a href="https://agentmods.dev/skills/leeroo-ai/leeroopedia-mcp/ml_inference_optimization"><img src="https://agentmods.dev/badge/skills/leeroo-ai/leeroopedia-mcp/ml_inference_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/leeroo-ai/leeroopedia-mcp/ml_inference_optimization"><img src="https://agentmods.dev/badge/skills/leeroo-ai/leeroopedia-mcp/ml_inference_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 | $0.00000 | $0.00723 |
| Opus 5 | $0.00000 | $0.00362 |
| Sonnet 5 | $0.00000 | $0.00145 |
| Haiku 4.5 | $0.00000 | $0.00072 |
Grade A, and why
ml_inference_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 10d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Leeroopedia Knowledge Base — Tool Usage Reference
This document describes the Leeroopedia MCP tools available during the with-KB benchmark run. It is a standalone reference and is not fed to the agents automatically.
Leeroopedia Knowledge Base (MANDATORY)
You have access to the Leeroopedia MCP tools. The KB contains production-grade CUDA and Triton kernel implementations from TransformerEngine, DeepSpeed, vLLM, Bitsandbytes, ggml, Ncnn, and MNN.
You MUST use these tools as part of your workflow for every problem. Do not rely solely on your training knowledge — the KB contains hardware-specific optimization patterns, tested block sizes, and proven kernel designs that will produce better results than writing kernels from scratch.
Required Per-Problem Workflow
For each of the 10 problems, follow this sequence:
-
Search — Call
search_knowledgewith the problem's operation, tensor shapes, and target GPU. Example:search_knowledge("fused LayerNorm + GELU kernel for 5D tensor (32, 64, 32, 64, 64), reducing over last dim=64, NVIDIA L4 Ada Lovelace") -
Hypothesize — Call
propose_hypothesiswith 2-3 candidate approaches and the reference timing. Let the KB rank them before you commit. -
Plan — Call
build_planwith your chosen approach and exact dimensions to get concrete thread block sizes, shared memory layout, and reduction strategy. -
Write — Implement the kernel using the plan from step 3.
-
Review — Call
review_planwith your kernel code before running evaluation. Fix any issues it identifies (wrong indexing, missing sync barriers, race conditions). -
On failure — If evaluation fails (compile error or incorrect results), call
diagnose_failurewith the exact error message and your kernel code. If numerical correctness fails, also callverify_code_mathwith the tensor shapes and reduction dimensions.
Tool Quick Reference
| Tool | Purpose |
|---|---|
search_knowledge |
Find relevant kernel implementations and optimization patterns |
propose_hypothesis |
Rank candidate optimization approaches |
build_plan |
Get concrete implementation plan (block dims, shared mem, reductions) |
review_plan |
Review kernel code for correctness bugs before testing |
verify_code_math |
Check numerical correctness (softmax stability, normalization, etc.) |
diagnose_failure |
Diagnose compilation errors or incorrect results |
get_page |
Retrieve full KB page when a tool response cites a [PageID] |
What ships with it
5 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.
- 10d ago First seen · 56 lines · 0 tokens per session scan A 3c0f40562d41
ml_inference_optimization is a skill published in the GitHub repository Leeroo-AI/leeroopedia-mcp (14 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 723 tokens. 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.
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