Data Analysis

social-graph-ranker is the weighted graph-ranking layer for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use the social-graph-ranker skill when you need a reusable ranking engine for Lead Research, not a full outbound or network-maintenance workflow.

regex-vs-llm-structured-text skill for choosing regex or LLM in structured text extraction. Start with deterministic parsing, add LLM validation for low-confidence edge cases, and use a cheaper, more reliable pipeline for documents, forms, invoices, and data analysis.

clickhouse-io is a ClickHouse-focused skill for schema design, analytical SQL, ingestion patterns, and performance tuning. Use it to guide MergeTree choices, partitioning, materialized views, and workload-specific query optimization.

data-analyst is a minimal GitHub skill that guides agents toward SQL, pandas, and basic statistical analysis for data exploration. Best for users who want code-backed queries, transformations, and interpretations from a single SKILL.md prompt layer.

retro is a project retrospective skill for engineering teams. It analyzes commit history, work patterns, and prior learnings to generate a structured weekly retro with continuity. Use retro for sprint reviews, what did we ship questions, and Project Management check-ins.

xlsx is a Claude spreadsheet skill for creating, editing, analyzing, formatting, and recalculating .xlsx, .xlsm, .csv, and .tsv files. It emphasizes zero formula errors, preserving existing workbook templates, financial-model formatting conventions, and LibreOffice-based recalculation with recalc.py.

textrazor-automation is a Claude skill for automating Textrazor tasks through Composio Rube MCP, with tool discovery, connection checks, and schema-aware execution.

rosette-text-analytics-automation helps agents run Rosette Text Analytics through Composio Rube MCP by checking connections, discovering live tool schemas with RUBE_SEARCH_TOOLS, and executing text analysis workflows.

microsoft_clarity-automation helps agents use Microsoft Clarity through Composio Rube MCP for session recordings, heatmaps, and behavior analytics. It emphasizes RUBE_SEARCH_TOOLS first, active Clarity connection setup, and schema-aware usage.

googlebigquery-automation helps agents use Rube MCP and Metabase to access BigQuery data, verify connections, inspect metadata, and run native SQL or MBQL analysis without guessing schemas.

eodhd-apis-automation helps agents automate EODHD API workflows through Composio Rube MCP, with tool discovery first, connection checks, and schema-aware execution.

college-football-data-automation helps agents automate College Football Data tasks through Rube MCP and Composio. Learn setup requirements, tool discovery with RUBE_SEARCH_TOOLS, connection checks, and practical usage patterns.

startup-metrics-framework helps founders, analysts, and operators calculate startup KPIs like CAC, LTV, burn multiple, runway, and growth metrics for SaaS, marketplace, consumer, and B2B startups.

Use the market-sizing-analysis skill to build structured TAM, SAM, and SOM estimates with top-down, bottom-up, and value-theory methods. Covers install context, key files, inputs, workflow, and practical usage for startup market sizing and Data Analysis.

startup-financial-modeling helps agents build 3-5 year startup finance models with cohort revenue, cost structure, burn, runway, and fundraising scenarios. Best for founders and finance leads who need install context, clear inputs, and practical usage guidance from the skill's SKILL.md.

risk-metrics-calculation helps compute portfolio risk metrics like VaR, CVaR, Sharpe, Sortino, beta, volatility, and drawdown. Use it to turn return series into structured risk reporting, Python implementation patterns, and practical interpretation for finance workflows.

The backtesting-frameworks skill helps design and review trading strategy backtests with stronger controls for look-ahead bias, survivorship bias, overfitting, transaction costs, and walk-forward validation in Finance.

spark-optimization is a practical guide to diagnosing slow Apache Spark jobs with partitioning, shuffle, skew, caching, and memory tuning. Use it to install the skill from wshobson/agents, read SKILL.md, and apply evidence-based fixes from Spark UI symptoms, cluster settings, and query patterns.

product-analytics helps agents define KPIs, select AARRR, North Star, or HEART frameworks, design dashboards, and analyze retention, cohorts, funnels, and feature adoption for Product Management workflows.

experiment-designer helps Product Management teams design A/B and feature experiments with testable hypotheses, metrics, sample-size planning, ICE prioritization, stopping rules, and result interpretation.

senior-data-scientist is a Claude skill for A/B test design, causal reasoning, feature engineering, and tabular ML evaluation. Use it to guide sample sizing, metric choice, leakage checks, SHAP review, and MLflow-style tracking; scripts are scaffold templates, not complete engines.

channel-economics helps RevOps and commercial leaders compare direct, partner, marketplace, reseller, or OEM channels with fully loaded cost-to-serve, ROI lenses, and constrained channel-mix recommendations. Includes Python scripts, data templates, and guidance for channel-economics usage.

torchdrug is a PyTorch-native toolkit for molecular and protein machine learning. Use the torchdrug skill to choose tasks, datasets, and modular models for graph neural networks, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis. It is best for custom model development and reproducible configs, not just canned demos.

torch-geometric skill guide for PyTorch Geometric graph neural networks. Use it for torch-geometric install help, torch-geometric usage, graph classification, node classification, link prediction, heterogeneous graphs, custom MessagePassing layers, and scaling GNNs for Machine Learning workflows.