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signal-classificationagiprolabsML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization walk-forward-validationagiprolabsWalk-forward validation framework for trading strategies and ML models with time-series-aware splits, overfit detection, and regime-aware validation wallet-profilingagiprolabsBehavioral classification, performance analysis, and trading style detection for Solana wallets ads-researchagricidanielRefresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refresh_due dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or updating paid-media knowledge. When tools or sources are unavailable, still demote the stale claim for the current run and block dependent release-current assertions before requesting a autoresearchagricidanielRun a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.aicoin-hyperliquidaicoincomHyperliquid on-chain perpetuals analytics from AiCoin Open API v3 — the primary source for on-chain whale / smart-money / large-fund movement. Use this skill when the user asks about: Hyperliquid whale positions, HL liquidations, HL open interest, HL trader analytics, HL taker flow, HL funding history, AND generic on-chain whale activity — '链上大资金动向', '链上鲸鱼', '聪明钱', '大户在干嘛', 'on-chain whale', 'smart money', 'Hyperliquid大户', 'HL鲸鱼', 'HL持仓', 'HL清算', 'HL持仓量', 'HL交易员', 'HL 资金费率' — because HL is the d bilibili-analyzeraidotnet自动分析B站视频内容，下载视频并拆解成帧图片，使用AI分析并生成详细的专题文档或实操教程。data-analystailabs-393This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.Anomaly Detectionaj-geddesIdentify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring Causal Inferenceaj-geddesDetermine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects Classification Modelingaj-geddesBuild binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification Clustering Analysisaj-geddesIdentify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning Correlation Analysisaj-geddesMeasure relationships between variables using correlation coefficients, correlation matrices, and association tests for correlation measurement, relationship analysis, and multicollinearity detection Data Cleaning Pipelineaj-geddesBuild robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pipeline automation Data Visualizationaj-geddesCreate effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholders Dimensionality Reductionaj-geddesReduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency Exploratory Data Analysisaj-geddesDiscover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, data profiling, and initial insights Feature Engineeringaj-geddesCreate and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability ML Model Explanationaj-geddesInterpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability Model Hyperparameter Tuningaj-geddesOptimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt Network Analysisaj-geddesAnalyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures Recommendation Systemaj-geddesBuild collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement Regression Modelingaj-geddesBuild predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification Sentiment Analysisaj-geddesClassify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis Statistical Hypothesis Testingaj-geddesConduct statistical tests including t-tests, chi-square, ANOVA, and p-value analysis for statistical significance, hypothesis validation, and A/B testing Survival Analysisaj-geddesAnalyze time-to-event data, calculate survival probabilities, and compare groups using Kaplan-Meier and Cox proportional hazards models Time Series Analysisaj-geddesAnalyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models user-research-analysisaj-geddesAnalyze user research data to uncover insights, identify patterns, and inform design decisions. Synthesize qualitative and quantitative research into actionable recommendations.data-analysisakillnessAnalyze datasets to extract insights, identify patterns, and generate reports.huashu-data-proalchaincyf数据分析与办公提效全能助手。覆盖数据处理、分析洞察、报告撰写、PPT制作、数据可视化的端到端工作流。始终从专家视角出发，帮用户多想一步。遇到不确定的问题主动与用户确认。支持：Excel数据分析、投放数据复盘、ROI测算、数据可视化、报告生成、PPT制作、公式生成。当用户提到"分析数据"、"做报告"、"做PPT"、"Excel"、"投放分析"、"ROI"、"复盘"、 "周报"、"月报"、"数据处理"、"图表"、"可视化"、"汇报"、"表格"、"公式"时使用此技能。huashu-info-searchalchaincyf多渠道搜索新产品新技术，交叉验证后存入知识库。当用户提到"最新信息"、"新产品"、"搜索资料"、"查资料"、"了解XX"时使用。huashu-researchalchaincyf结构化网络调研流程，确保调研成果增量保存到文件，不因会话截断丢失。当用户说"调研"、"搜索资料"、"帮我查一下"、"了解一下"、"最新信息"时使用此技能。data-quality-auditoralirezarezvaniAudit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan. Use when the user asks to check data quality, profile a dataset, hunt outliers or missing values, or validate data before analysis or model training.litreviewalirezarezvaniAcademic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing check research-summarizeralirezarezvaniStructured research summarization agent skill for non-dev users. Handles academic papers, web articles, reports, and documentation. Extracts key findings, generates comparative analyses, and produces properly formatted citations. Use when: user wants to summarize a research paper, compare multiple sources, extract citations from documents, or create structured research briefs. Plugin for Claude Code, Codex, Gemini CLI, and OpenClaw.runalirezarezvaniRun a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.senior-data-scientistalirezarezvaniWorld-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled expe statistical-analystalirezarezvaniRun hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.stock-analysisalphamoemoeGenerate a comprehensive sentiment analysis report for a single stock. Use when users want deep analysis of a specific ticker like NVDA, TSLA, or AAPL.exam-forecastanthropicsAnalyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.research-startanthropicsResearch roadmap for a legal issue — statutes to check, case law areas to investigate, regulatory frameworks, Westlaw search terms. Leads and frameworks, NOT authoritative citations; students verify and develop everything. Use when a student asks where to start researching, wants a research roadmap for an issue, or needs gaps identified in existing research.sector-overviewanthropicsCreate comprehensive industry and sector landscape reports covering market dynamics, competitive positioning, key players, and thematic trends. Use for client requests, sector initiations, thematic research pieces, or internal knowledge building. Triggers on "sector overview", "industry report", "market landscape", "sector analysis", "industry deep dive", or "thematic research".anycap-deepresearchanycap-aiGuide for conducting thorough, multi-source research and producing comprehensive, well-sourced reports. Powered by AnyCap -- the capability runtime that equips AI agents with web search (including AI Grounded citations), web crawl, image generation, cloud storage, and one-click web publishing through a single CLI. Use when the user asks for deep research, competitive analysis, market research, technical deep dive, literature review, technology comparison, or any task requiring multi-source infor discovering-pre-launch-startups-on-twitterapidojo-ioDiscovers pre-launch startups and products on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: find pre-launch startups on Twitter, discover companies building in stealth mode on X, find products in beta or waitlist mode on Twitter, identify early-stage startups before they launch publicly, find founders building in public before launch, discover startup waitlists or beta invites on Twitter, or research what new companies are building in a space. Returns startup ha researching-internet-slang-and-cultural-trendsapidojo-ioExtracts slang definitions, cultural terms, and crowd-sourced meanings from Urban Dictionary using apidojo's Urban Dictionary Scraper on Apify. Triggers when the user asks to: look up internet slang terms, find how Gen Z or millennials define a word, research cultural vocabulary or meme terminology, understand what a term means on social media, analyze slang used in brand monitoring, find crowd-sourced definitions for multiple keywords, or track how language is evolving around a topic or brand n profiling-tablesastronomerDeep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.search-company-knowledgeatlassianSearch across company knowledge bases (Confluence, Jira, internal docs) to find and explain internal concepts, processes, and technical details. When an agent needs to: (1) Find or search for information about systems, terminology, processes, deployment, authentication, infrastructure, architecture, or technical concepts, (2) Search internal documentation, knowledge base, company docs, or our docs, (3) Explain what something is, how it works, or look up information, or (4) Synthesize information whybacknotpropUse for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.memory-researchbasicmachines-coResearch an external subject using web search, synthesize findings into a structured Basic Memory entity. Use when asked to research a company, person, technology, or topic — or when a bare name or URL is provided that implies a research request.memory-researchbasicmachines-coResearch an external subject using web search, synthesize findings into a structured Basic Memory entity. Use when asked to research a company, person, technology, or topic — or when a bare name or URL is provided that implies a research request.