Data & Analytics

--> --- name: bio-molecular-descriptors description: Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria. tool_type: python primary_tool: RDKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_...

--> --- name: bio-admet-prediction description: Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety. tool_type: python primary_tool: ADMETlab measurable_outcome: Execute skill workflow successfully with valid output within 15...

--> --- name: bio-hi-c-analysis-tad-detection description: Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores. tool_type: mixed primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Call topologically asso...

--> --- name: bio-hi-c-analysis-loop-calling description: Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data. tool_type: mixed primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Detect chromatin loops an...

--> --- name: bio-hi-c-analysis-hic-visualization description: Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Visualize Hi-C c...

--> --- name: bio-hi-c-analysis-hic-differential description: Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Comp...

--> --- name: bio-hi-c-analysis-contact-pairs description: Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs. tool_type: cli primary_tool: pairtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Process Hi-C read pairs with pairtools.

--> --- name: bio-hi-c-analysis-compartment-analysis description: Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Detect A/B compartment...

Panel data analysis with fixed and random effects models

10 econometrics skills. Trigger: causal analysis, regression models, treatment effects, panel data. Design: method-centric guides with R/Python code and diagnostic tests.

Create slide presentations in multiple formats: Marp (Markdown, default), Beamer (LaTeX), Jupyter Notebook slides, and PowerPoint (python-pptx). Supports scientific, business, and developer content with math, code, charts, and tables. Converts existing documents into slides or creates from scratch. Auto-compiles to PDF/HTML/PPTX. Use this skill when the user requests: - Creating a presentation or slide deck - Converting a document, paper, or report into slides - Making conference talk, lectu...

Professional FIFA World Cup match intelligence system — 8-layer research architecture with China Sports Lottery market formats and beautiful HTML reports.

Use when Codex should act as the Analytics Reporter specialist from Agency Agents. Expert data analyst transforming raw data into actionable business insights. Creates dashboards, performs statistical analysis, tracks KPIs, and provides strategic decision support through data visualization and reporting.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

Writes idiomatic tidyverse R for data analysis - dplyr wrangling pipelines, tidyr reshaping, explicit joins, layered ggplot2 visualization, and broom-tidied statistical models - with reproducibility practices baked in. Use when someone asks "write this analysis in R", "how do I pivot this data frame", "fit a regression per group in R", or wants messy base-R scripts converted to clean pipe-based tidyverse code. Do NOT use for Python-based dataframe work - use pandas-expert instead; for interpr...

When the user needs to classify countries according to World Bank geographical or income-based classifications for economic analysis. This skill provides accurate mappings between country names and World Bank region codes (EAS, ECS, LCN, MEA, NAC, SAS, SSF) and income groups (LIC, LMC, UMC, HIC), handling variations in country naming conventions. Triggers include 'World Bank classification', 'map countries to regions', 'regional grouping', 'country classification', 'geographical regions', and...

When the user requests anomaly detection in numerical datasets using statistical methods such as mean and standard deviation thresholds (e.g., 'amount > mean + 3*std'). This skill calculates per-group statistics, identifies outliers, and flags abnormal records. It is triggered by keywords like 'anomaly detection', 'outlier identification', 'statistical threshold', 'abnormal transactions', or any request involving standard deviation-based filtering in financial or transactional data.

Calculates price spreads, MoM% changes, and rolling z-scores for pair trading analysis. Implements statistical normalization with configurable parameters.

Analyzes sports league schedules to extract statistical insights about team schedules, including back-to-back games, home/away patterns, travel analysis, or schedule density. Processes schedule data from spreadsheets or structured sources, identifies consecutive game patterns, categorizes them by location configurations (HA, AH, HH, AA), and generates team-by-team breakdowns.

Build and visualize a citation network from a source/target CSV to identify key papers, communities, and emerging hotspots; use when you have citation pairs and need fast literature review or trend analysis.