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Data & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 1,945–1,968 of 13,012 skills
- A2a ProtocolLiteLLM-RS A2A Protocol Architecture. Covers Agent-to-Agent communication, JSON-RPC 2.0 messaging, multi-provider orchestration, agent registry, and task state management.Votes: 0GitHub stars: 2
- 06 Mofa Studio SettingsProvider settings and user preferences in MoFA Studio. Use when editing provider UI, saving preferences, or wiring API keys and defaults.Votes: 0GitHub stars: 2
- 学术文本英译中将英文学术文本（涵盖生物信息学、医学及人文社科）翻译为正式的中文论文语言，保持术语准确性和逻辑结构。Votes: 0GitHub stars: 2
- 使用Tensorflow Java 0 4 0进行Savedmodel预测针对TensorFlow Java 0.4.0版本，实现加载SavedModel模型，将三维double数组转换为Tensor并执行预测返回double数组的逻辑。Votes: 0GitHub stars: 2
- Tensorflow Java Gpu环境配置与故障排查提供在Windows环境下使用TensorFlow for Java进行GPU计算的完整配置指南，包括Maven依赖、代码示例、CUDA/cuDNN版本兼容性检查，以及针对JDK 17的DLL加载路径设置方案。Votes: 0GitHub stars: 2
- Pytorch文本分类键盘输入推理脚本生成生成用于PyTorch LSTM文本分类模型的推理代码，支持键盘输入、jieba分词、词汇表映射及情感标签输出。Votes: 0GitHub stars: 2
- 计算数据的平均数 标准差及P值用于计算给定数据集的平均数、标准差以及两组数据间的P值。当用户要求“无需过程”或“只需结果”时，仅输出最终数值。Votes: 0GitHub stars: 2
- Tensorflow 训练代码内存优化与修复针对TensorFlow训练代码进行内存泄漏修复，包括优化数据管道、添加每轮结束后的垃圾回收回调以及修正ModelCheckpoint配置。Votes: 0GitHub stars: 2
- Pytorch中文文本分类预测脚本编写编写用于加载词汇表、使用Jieba分词、构建TextDataset类以及执行LSTM模型预测的Python代码。Votes: 0GitHub stars: 2
- TransformersWork with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, Q...Votes: 0GitHub stars: 2
- Performance OptimizationApply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.Votes: 0GitHub stars: 2
- Data VisualizationCreates effective data visualizations using various libraries and tools, with focus on clarity and insight communication. Trigger keywords: chart, graph, plot, visualization, dashboard, matplotlib, d3, plotly, visualization.Votes: 0GitHub stars: 2
- Mapping High Involvement Purchase JourneysResearch-intensive purchase mapping: build a customer decision journey map for a high-involvement purchase from buyer interviews and behavioral data.Votes: 0GitHub stars: 2
- Interpreting Bayesian And Frequentist Experiment ResultsRead PostHog experiment results under Bayesian or frequentist stats: win probability, credible intervals, p-values and what counts as significant.Votes: 0GitHub stars: 2
- Building Planned Journey Funnel VisualizationsBuild a planned journey funnel diagram that shows stakeholders how buyers flow through each stage, where drop-off happens and why.Votes: 0GitHub stars: 2
- Posthog Measurements For Goal BindingsStandardizes creating and debugging PostHog insights for Hamster goal measure bindings. Covers TrendsQuery vs HogQL, the measure binding contract (aggregated_value), math types, formulas, HogQL pitfalls, and the Composio API actions needed. Use when binding a PostHog metric to a goal, debugging a measure showing 0/null, or converting HogQL to TrendsQuery.Votes: 0GitHub stars: 2
- Goal Health ReviewGuides the weekly goal review meeting: compare goals vs initiatives, prioritize newly unblocked work, assign owners. Enforces the 'one weekly meeting' principle — 30 minutes of decisions, not 45 minutes of status. Use when running a weekly goal review, planning meeting, or direction review.Votes: 0GitHub stars: 2
- Brief Alignment GatingGoverns the alignment voting gate between direction and delivery. Enforces that no brief enters delivery without explicit team alignment. Use when calling for alignment, interpreting vote results, or deciding if a brief is ready for delivery.Votes: 0GitHub stars: 2
- Find CFlashbangProjectile Spawn DecompilesFind and identify the CBaseModelEntity_SetModel and CBaseEntity_SetGravityScale functions in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll / libserver.so to locate the model-assignment routine by scanning for its distinctive fixed byte signature directly (two near-identical sibling wrappers exist and must be disambiguated by matching the reference signature's exact tail bytes, including the following function's leading byte). Trigger: CBaseModelEntity_Se...Votes: 0GitHub stars: 3
- Evident ChartsUse when creating, revising, or reviewing explanatory data charts for a blog, report, paper, slide, newsletter, or social post. Includes matplotlib, seaborn, pandas .plot, ggplot2, Plotly, Altair/Vega-Lite, D3, and PNG/SVG charts. Also use for "critique this chart", "why does this plot look bad", or "make this figure better" with a chart image. Not for unrelated coding, software architecture diagrams, or non-data illustrations.Votes: 0GitHub stars: 57
- Exploratory Data AnalysisInspect a scientific data file and report its structure and quality. Use when triaging an unknown table, array, sequence or image file.Votes: 0GitHub stars: 9
- Zhang Lei Longterm张磊高瓴长期结构性价值投资：A股4维成长质量雷达，锁定连续双增、长期投入、行业空间、ROE向上的“时间的朋友” | Zhang Lei Hillhouse long-term structural value investing: 4-dimensional growth-quality radar for A-shares targeting sustained double growth, R&D/capex investment, industry headroom, and rising ROE | 張磊・高瓴の長期構造価値投資：A株4次元成長質スクリーナー、連続増収増益・長期投資・業界成長余地・ROE上昇を捕捉Votes: 0GitHub stars: 19
- Qiu Guolu Simple RulesA股邱国鹭「投资中最简单的事」三好龙头打分器：好行业、好公司、好价格，数月亮不数星星 | A-share Qiu Guolu-style simple investing: good industry, good company, good price — count moons, not stars | A株邱国禄「投資で最も単純なこと」：好業種・好企業・好価格で業界龙头をスコアリングVotes: 0GitHub stars: 19
- Neff Low Pe Total ReturnA股约翰·聂夫低市盈率+总回报率投资法：个股PE相对全市场中位数折价、(增速+股息率)/PE总回报率≥2黄金标准、股息率、盈利增长底线、ROE、低负债、含金量七维打分，找"遭人嫌弃的好公司" | A-share John Neff low-P/E + total-return investing: PE discount vs market median, total-return ratio ≥2, dividend yield, growth floor, ROE, low leverage, cash quality | A株ジョン・ネフ低PER+総リターン投資法：市場中央値比PER割引、総リターン比率≥2、配当利回り、成長下限、ROE、低負債、キャッシュの質の7次元Votes: 0GitHub stars: 19