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 Bruce-Lee-LY/cuda_auto_tune --skill cuda-auto-tunegit clone --depth 1 https://github.com/Bruce-Lee-LY/cuda_auto_tuneWrote 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/bruce-lee-ly/cuda_auto_tune/cuda-auto-tune)<a href="https://agentmods.dev/skills/bruce-lee-ly/cuda_auto_tune/cuda-auto-tune"><img src="https://agentmods.dev/badge/skills/bruce-lee-ly/cuda_auto_tune/cuda-auto-tune/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/bruce-lee-ly/cuda_auto_tune/cuda-auto-tune"><img src="https://agentmods.dev/badge/skills/bruce-lee-ly/cuda_auto_tune/cuda-auto-tune.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 | $0.00167 | $0.06005 |
| Opus 5.5 | $0.00067 | $0.02402 |
| Sonnet 5.5 | $0.00033 | $0.01201 |
| Haiku 4.5 | $0.00017 | $0.00600 |
Grade C, and why
cuda-auto-tune scanned grade C with 1 finding 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 2mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf __pycache__/ .cache/ /tmp/cutlass_cute_cache/ How it starts
The opening of the file, as written. The whole thing — 495 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NCU-driven iterative kernel optimization (CUDA / CUTLASS / Triton / CuTe DSL)
GATE CHECK (enforce before any optimization)
STOP — Do you have NCU profile data for this kernel?
NO → Go to Step 1. Do NOT touch any kernel code.
YES → Go to Step 2.
Hard rules — violation of any rule invalidates the entire optimization:
- NEVER change kernel code, launch config, or template parameters without NCU data.
- ALL recommendations MUST cite specific NCU metric values as evidence.
- Each iteration MUST cover at minimum: roofline, memory hierarchy, warp stalls, occupancy.
- The optimization playbook MUST match the kernel implementation type.
- After EVERY code change, re-profile and compare with
--diff. - Stop iterating when improvements plateau or metrics approach hardware ceiling.
Mandatory optimization loop
┌─────────────────────────────────────────────────────────────────────┐
│ Step 1: Profile (NCU --set full) │
│ ↓ │
│ Step 2: Multi-dimensional analysis + identify kernel type │
│ ↓ │
│ Step 3: Apply type-specific playbook (one change per iteration) │
│ ↓ │
│ Step 4: Re-profile + diff → improved? → loop or stop │
│ ↑ │ │
│ └───────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
Step 1: Profile with NCU (REQUIRED — no data = no optimization)
Option A: Profiling script (recommended)
# Native CUDA / CUTLASS binaries
bash cuda-auto-tune/scripts/ncu_profile.sh ./kernel report_v1
# Triton / Python
bash cuda-auto-tune/scripts/ncu_profile.sh "python your_kernel.py" report_v1
# CuTe DSL / Python
bash cuda-auto-tune/scripts/ncu_profile.sh "python your_cutedsl_kernel.py" report_v1
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.
- 2mo ago First seen · 495 lines · 167 tokens per session scan C 2b7f66fff6c2
cuda-auto-tune is a skill published in the GitHub repository Bruce-Lee-LY/cuda_auto_tune (25 stars, last pushed 7mo ago), licensed MIT. It adds 167 tokens to every session and 6,005 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
cuda-auto-tune compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cuda-auto-tuneBruce-Lee-LY/cuda_auto_tune | Skill | 25 | 7mo ago | MIT | C | 167 | Claude Code, Codex |
| spark-environment-setupwshobson/agents | Skill | 42k | yesterday | MIT | A | 76 | Claude Code |
| spark-memory-thermal-opswshobson/agents | Skill | 42k | yesterday | MIT | A | 59 | Claude Code |
| spark-training-gotchaswshobson/agents | Skill | 42k | yesterday | MIT | A | 63 | Claude Code |
| terraform-module-librarywshobson/agents | Skill | 42k | yesterday | MIT | A | 44 | Claude Code |
| auditing-uefi-firmware-with-chipsecadriannoes/awesome-agentic-ai | Skill | 96 | 4d ago | MIT | A | 56 | Claude Code, Codex |
| lov-migrate-camera-medialovstudio/skills | Skill | 72 | 3d ago | MIT | A | 86 | Claude Code |