End-to-end data analysis AI agent with Streamlit UI

Clean, recode, and prepare survey response data for analysis

6 geoscience & climate skills. Trigger: earth science data, GIS, remote sensing, climate modeling. Design: geospatial tools, satellite data processing, and environmental models.

5 physics & astrophysics skills. Trigger: physics simulations, astronomical data, computational physics. Design: domain databases (NASA ADS, arXiv) and simulation tool guides.

Convert Python, JavaScript, and TypeScript functions into Mermaid flowcharts

Guide to JSON Crack for visualizing complex JSON data structures

Quick reference for Portfolio Buddy 2 project structure. Use when: adding new features, modifying existing components, understanding data flow, or onboarding to the codebase. Contains component hierarchy, hook patterns, and utility functions.

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Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.

Parse, transform, and analyze CSV files with advanced data manipulation capabilities.

Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automation script, ai orchestration

Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

python-executor

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Read analysis results. Use when user asks about maximum stress, extracting displacements, reaction forces, or exporting results. Post-processes ODB files.

When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

Advanced analytics including machine learning, predictive modeling, and big data techniques

Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment

Implement comprehensive audit logging for all admin actions, capturing user ID, action type, entity changes, IP address, and user agent. Use when tracking system activities or adding audit trails.

Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.

Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.

Analyzes bear-put-spread debit spreads for bearish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price decline, comparing put spread configurations, analyzing debit spread opportunities, or evaluating defined-risk bearish positions on mid to large-cap stocks.

Compare and analyze contractor bids. Score proposals, identify scope gaps, and recommend selections.

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Query Danish real estate data from Boliga.dk as pandas DataFrames. Use when the user asks about Danish property prices, real estate searches, market statistics, or housing analysis in Denmark.

Price fixed income securities using present value, yield-to-maturity, and market conventions. Handles treasuries, corporates, municipals with various coupon frequencies. Requires numpy>=1.24.0, pandas>=2.0.0, scipy>=1.10.0. Use when valuing bonds, calculating accrued interest, or analyzing price sensitivity to yield changes.

Analyze personal or business expenses from CSV/Excel. Categorize spending, identify trends, compare periods, and get savings recommendations.

Systematic bug investigation and root cause analysis

Analyzes bull-call-spread debit spreads for bullish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price increase, comparing vertical spread configurations, analyzing debit spread opportunities, calculating Greeks for multi-leg positions, or evaluating defined-risk bullish strategies on liquid optionable securities.

Answer analytical questions about carbon accounting data using internal datasets, APIs, and emission factor calculations.

Cell Ranger skill for 10X Genomics single-cell data processing

Calculate and track CO2 emissions and carbon footprint for construction projects.

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.

This skill provides systematic fact-checking, source verification, and quality assurance methodologies for competitive research deliverables. It works as a companion to the competitive-research-bright

Assess credit risk and default probability for bonds using credit spreads, rating transitions, and recovery analysis. Requires numpy>=1.24.0, pandas>=2.0.0, scipy>=1.10.0. Use when evaluating corporate bonds, analyzing credit events, estimating default probabilities, or managing credit portfolio risk.

Handle messy CSVs with encoding detection, delimiter inference, and malformed row recovery.

JTBD(Jobs To Be Done) 프레임워크를 사용하여 타겟 고객 유형을 정의합니다. 고객 세그먼트와 핵심 job을 이해할 때 사용하세요.

Use when creating maps, working with geographic projections, or processing GeoJSON data. Invoke for world maps, choropleth maps, projection types, geo path generators, spherical geometry, or geographic feature manipulation.

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.

Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.

data-analyst-export

Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.

Clean and standardize vehicle insurance data following established business rules.

Data cleaning, preprocessing, and quality assurance techniques

Data governance strategy, quality validation rules, and data dictionary management for vehicle insurance platform. Use when defining data quality standards, implementing validation rules, managing field mappings, resolving data conflicts, or establishing data governance processes. Covers data cleaning standards, quality metrics, and mapping management.

Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.

Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.

Connect your own data source to replace the demo unicorns data. Use when the user wants to use their own database URL or CSV file instead of the sample data. Triggers on requests to connect database, import CSV, change data source, use own data, or switch from demo data.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.