machine-learning
185
base-model-selector
Use when starting a fine-tuning project to determine if fine-tuning is needed, or when evaluating whether a base model meets quality thresholds for a specific domain task
majiayu000
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
185
baseline-quality-assessment
Achieve comprehensive baseline (V_meta ≥0.40) in iteration 0 to enable rapid convergence. Use when planning iteration 0 time allocation, domain has established practices to reference, rich historical data exists for immediate quantification, or targeting 3-4 iteration convergence. Provides 4 quality levels (minimal/basic/comprehensive/exceptional), component-by-component V_meta calculation guide, and 3 strategies for comprehensive baseline (leverage prior art, quantify baseline, domain universality analysis). 40-50% iteration reduction when V_meta(s₀) ≥0.40 vs <0.20. Spend 3-4 extra hours in iteration 0, save 3-6 hours overall.
majiayu000
data-ai
machine-learning
185
cns-tinker
Apply Chiral Narrative Synthesis (CNS) framework for contradiction detection and multi-source analysis using Tinker API for model training. Use when implementing CNS with Tinker for fine-tuning models on contradiction detection, training on SciFact/FEVER datasets, or building multi-agent debate systems for narrative synthesis.
majiayu000
data-ai
machine-learning
185
create-test-lane
This skill should be used when creating new test lanes for the XML test data generator. A test lane consists of an XSD schema file paired with a meta.yaml configuration file. This skill guides the process of creating both files with proper semantic type mappings, distribution settings, and field overrides. Use when users request new test lanes, want to generate test data configurations, or need help setting up XSD + meta.yaml pairs for the testgen CLI tool.
majiayu000
data-ai
machine-learning
185
data-ai-ml-skill
Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.
majiayu000
data-ai
machine-learning
185
data-ai-skills
Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.
majiayu000
data-ai
machine-learning
185
huggingface-model-trainer
Train and fine-tune LLMs using HuggingFace TRL, Transformers, and cloud GPU infrastructure with SFT, DPO, GRPO methods
majiayu000
data-ai
machine-learning
185
laravel-models
Eloquent model patterns and database layer. Use when working with models, database entities, Eloquent ORM, or when user mentions models, eloquent, relationships, casts, observers, database entities.
majiayu000
data-ai
machine-learning
185
baseline-runner
Use this when the project needs real baseline results before or alongside the main model. Runs classical or literature-aligned baselines under the same protocol and writes a reproducible baseline summary.
tsingyuai
data-ai
machine-learning
185
research-pipeline
Use this skill when the user wants to research a topic, analyze papers, build ML models, or run experiments. Orchestrates the full pipeline: paper search → analysis → planning → implementation → review → experiments.
tsingyuai
data-ai
data-analysis
184
hugging-face-stat
专门用于获取 Hugging Face 上的模型、数据集和 Space 的详细统计信息，包括历史总下载量。
cafe3310
data-ai
data-analysis
183
google-sheets
Integrate with Google Sheets for spreadsheet management and data processing. Use when you need to: (1) create and update spreadsheets, (2) read and write cell data in ranges, (3) manage sheets and formatting, or (4) create charts and analyze data in spreadsheets.
refly-ai
data-ai
data-analysis
181
regression-analysis-modeling
Perform comprehensive regression analysis and predictive modeling using linear regression, decision trees, and random forests. Use when you need to predict continuous values like housing prices, sales forecasts, demand predictions, or any numerical target variables. Includes automated feature engineering, model comparison, and visualization with Chinese language support.
liangdabiao
data-ai
data-analysis
181
funnel-analysis
Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.
liangdabiao
data-ai
data-analysis
181
attribution-analysis-modeling
Perform multi-touch attribution analysis using Markov chains, Shapley values, and custom attribution models. Use when you need to analyze marketing channel effectiveness, calculate conversion attribution, optimize marketing budgets, or understand customer journey paths. Supports channel transition analysis, ROI calculation, and marketing optimization insights with Chinese language support.
liangdabiao
data-ai
data-analysis
181
rfm-customer-segmentation
Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.
liangdabiao
data-ai
data-analysis
181
recommender-system
智能推荐系统分析工具，提供多种推荐算法实现、评估框架和可视化分析。使用时需要用户行为数据、商品信息或评分数据，支持协同过滤、矩阵分解等推荐算法，生成个性化推荐结果和评估报告。
liangdabiao
data-ai
data-analysis
181
ab-testing-analyzer
全面的AB测试分析工具，支持实验设计、统计检验、用户分群分析和可视化报告生成。用于分析产品改版、营销活动、功能优化等AB测试结果，提供统计显著性检验和深度洞察。
liangdabiao
data-ai
data-analysis
181
data-exploration-visualization
自动化数据探索和可视化工具，提供从数据加载到专业报告生成的完整EDA解决方案。支持多种图表类型、智能数据诊断、建模评估和HTML报告生成。适用于医疗、金融、电商等领域的数据分析项目。
liangdabiao
data-ai
data-analysis
181
echarts-chart
ECharts数据可视化专家，能够根据数据和分析需求生成专业的ECharts图表配置。适用于：数据可视化、图表生成、数据展示、趋势分析图表、对比图表、分布图表、饼图、柱状图、折线图、散点图、雷达图、热力图、仪表盘、漏斗图、树形图、关系图、桑基图、数据报表图表、BI图表、数据大屏、仪表板图表、统计图表、分析图表、可视化报告。
xiaoyuge886
data-ai
llm-ai
180
agent-recorder-fixtures
Record real OpenAI/Anthropic HTTP back-and-forth (requests + responses, including streaming text/event-stream) and print paste-ready Swift fixtures for SwiftAgent unit tests (ReplayHTTPClient) using the AgentRecorder CLI or HTTPReplayRecorder. Use when adding/updating any provider adapter tests (text, streaming, structured outputs, tool calls), when payload formats change, or when debugging agent loop mismatches by inspecting recorded JSON/SSE payloads.
SwiftedMind
data-ai
data-analysis
177
sessiondashboard
Generate an interactive HTML dashboard of Claude Code session analytics
kagenti
data-ai