No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by harleyszhang · Agent Tool · ★ 119
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm_counts safe to install? View the security audit →
llmprofiler llm theoretical performance analysis tools and support params, flops, memory and latency analysis. 主要功能 支持 qwen2.5、qwen3 dense 系列模型。 支持张量并行推理模式。 支持 、、 等硬件以及主流 decoder-only 的自回归模型，可自行在配置文件中增加。 支持分析性能瓶颈，不同 是 还是 ，以及 的性能瓶颈。 支持输出每层和整个模型的参数量、计算量，内存和 。 推理时支持预填充和解码阶段分别计算内存和 latency、以及理论支持的最大 等等。 支持设置计算效率、内存读取效率（不同推理框架可能不一样，这个设置好后，可推测输出实际值）。 推理性能理论分析结果的格式化输出。 如何使用 使用方法，直接调用 文件中函数 函数并输入相关参数即可。 python def llmprofile(modelname="llama-13b", gpuname: str = "v100-sxm-32gb", bytesperparam: int = BYTESFP16, bs: int = 1, seqlen: int = 522, generatelen=1526, dszero: int = 0, dpsize: int = 1, tpsize: int = 1, ppsize: int = 1, spsize: int = 1, layernormdtypebytes: int = BYTESFP16, kvcachebytes: int = BYTESFP16, flopsefficiency: float = FLOPSEFFICIENCY, hbmmemoryefficiency: float = HBMMEMORYEFFICIENCY, intranodememoryefficiency=INTRANODEMEMORYEFFICIENCY, internodememoryefficiency=INTERNODEMEMORYEFFICIENCY, mode: str = "inference", ) - dict: """format print dicts of the total floating-point operations, MACs,
| Stars | 119 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 57.9863397002389/100 |
| Open Issues | 2 |
| Last Updated | 2025-07-11 |
| Created | 2023-07-26 |
| Platforms | python |
| Est. Tokens | ~505k |
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llm_counts is llm theoretical performance analysis tools and support params, flops, memory and latency analysis.. It is categorized as a Agent Tool with 119 GitHub stars.
llm_counts is primarily written in Python. It covers topics such as gpu-performance, llama, llm.
You can find installation instructions and usage details in the llm_counts GitHub repository at github.com/harleyszhang/llm_counts. The project has 119 stars and 10 forks, indicating an active community.
The top alternatives to llm_counts on Agent Skills Hub include LLMOne, ARIS-in-AI-Offer, vllm-cli. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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