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Data & AI

Machine learning, LLMs, and data processing.

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data-engineering

128

synthetic-data-generation

Generate synthetic data using sdg_hub with composable blocks and YAML flows. Use when the user wants to create training datasets, generate QA pairs, run data generation pipelines, build custom flows, produce synthetic data from documents, use agent frameworks for data generation, or distill MCP tool-use traces. Supports pre-built flows, custom Python scripts, and YAML flow authoring with 20+ blocks, agent connectors (Langflow, LangGraph), MCP tool-use, and 100+ LLM providers via LiteLLM.

Red-Hat-AI-Innovation-Team

data-ai

llm-ai

126

prompt-writing

Write effective prompts for AI systems — system prompts, agent instructions, skills, or any LLM prompt. Use when creating or improving prompts.

markmdev

data-ai

data-engineering

125

openspec-sync-specs

Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.

aehrc

data-ai

data-analysis

124

two-sample-mr-research-planner

Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers: "design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

aipoch

data-ai

data-analysis

124

hypothesis-generation

Structured scientific hypothesis formulation from observations; use when you have experimental observations or preliminary data and need testable hypotheses with predictions, mechanisms, and validation experiments.

aipoch

data-ai

data-analysis

124

hypogenic

Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.

aipoch

data-ai

data-analysis

124

spreadsheet-ops

Spreadsheet processing and analysis for CSV/Excel; trigger when users ask to merge/clean tabular data, run statistics, add/edit Excel formulas, apply formatting, generate charts, or force workbook recalculation.

aipoch

data-ai

data-analysis

124

plotly

Interactive visualization library for Python. Use it when you need hover tooltips, zoom/pan, selection, animations, or charts embeddable in web pages (e.g., dashboards, exploratory analysis, presentations).

aipoch

data-ai

data-analysis

124

multi-panel-figure-assembler

Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent labels, padding, and publication-ready DPI.

aipoch

data-ai

data-analysis

124

literature-statistics

Generate statistics for publication-year and journal distributions from local references or PDFs; use when you need standardized Year/Journal tables and a summary without any network access.

aipoch

data-ai

data-analysis

124

experimental-data-analysis

Perform statistical analysis on experimental data (descriptive stats, t-tests, ANOVA, multiple comparisons) when you need to interpret experimental results, assess statistical significance, or generate reproducible reports.

aipoch

data-ai

data-analysis

124

experimental-data-analysis

Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

aipoch

data-ai

data-analysis

124

spreadsheet-ops

Spreadsheet processing and analysis for CSV/Excel; trigger when users ask to merge/clean tabular data, run statistics, add/edit Excel formulas, apply formatting, generate charts, or force workbook recalculation.

aipoch

data-ai

data-analysis

124

clinic-sample-size

Unified tool for calculating sample sizes for Diagnostic, Efficacy, Etiology, and Prognosis clinical studies. Supports various statistical methods (Sensitivity/Specificity, Log-rank, Chi-square, EPV, etc.).

aipoch

data-ai

data-analysis

124

comparison-table-gen

Auto-generates comparison tables for concepts, drugs, or study results.

aipoch

data-ai

data-analysis

124

meta-radial-plot

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.

aipoch

data-ai

data-analysis

124

survival-curve-risk-table

Analyze data with `survival-curve-risk-table` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

aipoch

data-ai

data-analysis

124

open-targets-db

Query the Open Targets Platform to retrieve targets, diseases, or evidence records when you need target-disease association data and evidence-based scores for therapeutic discovery.

aipoch

data-ai

data-analysis

124

statistical-analysis

Guided statistical analysis for test selection, assumption checks, power analysis, and APA-style reporting. Use when you need to choose an appropriate statistical test for your data and produce publication-ready results (including effect sizes and diagnostics).

aipoch

data-ai

data-analysis

124

statistical-analysis-advisor

Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.

aipoch

data-ai

data-analysis

124

metagenomic-krona-chart

Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

aipoch

data-ai

data-analysis

124

meta-sensitivity-plot

Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.

aipoch

data-ai

data-analysis

124

meta-rob2-plot

Draw ROB2 risk-of-bias plots, including a Traffic Light Plot and a Summary Bar Plot. Input is a CSV file with ROB2 assessments for each study; output are two PNG plot files.

aipoch

data-ai

data-analysis

124

meta-picos-generator

Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.

aipoch

data-ai

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