3. Data, Science & AI

Provides critical PostgreSQL SQL pattern guidance for CRM database interactions, preventing errors with MCP__POSTGRESQL__ tools.

Enables querying and analysis of Ant Colony Optimization pheromone graphs in Memgraph for path validation and convergence monitoring in optimization problems.

Automates CRM file insertion and linking with base64 encoding, hashing, MIME detection, and SQL processing via Python.

Manages SQLite database operations including schema migrations, transactions, FTS5 search, and data integrity for GMailArchiver.

Builds autonomous AI agents on Cloudflare edge using prompt-defined features instead of code, leveraging Durable Objects and Workers for self-modifying systems.

Analyzes AI conversation exports to identify recurring patterns and generate custom Claude Skills for reusable workflows.

Enables LLM agent evaluation and safety assessment through MLflow 3.x, supporting scorers, datasets, and continuous quality monitoring.

Analyzes text content from meeting notes, emails, and documents to extract key insights and patterns.

Analyzes podcast episodes from transcripts or audio files to extract key topics, themes, and insights for content understanding.

Extracts summaries, key takeaways, and insights from YouTube video transcripts.

Processes multiple content items (videos, articles, papers) in batch from a list file, enabling efficient bulk analysis and data processing for research or AI applications.

Provides best practices for dbt (data build tool) in data modeling, testing, documentation, and project organization.

Creates specialized AI sub-agents with domain expertise using YAML configuration and defined scope boundaries following the Guild system's patterns.

Automates exploratory data analysis on user datasets, identifying structure, quality, and distribution patterns to guide further analysis.

Comprehensive toolkit for statistical analysis including hypothesis testing, regression, correlation, Bayesian methods, and APA reporting for research data.

Estimates power spectral density to analyze frequency components in time-series data, essential for signal processing and engineering applications.

Splits datasets into training and testing sets for machine learning model validation and evaluation.

Executes UAF-specific data preparation and validation workflows for time series datasets, ensuring analytical readiness.

Provides time series visualization and diagnostic plotting utilities for data analysis and interpretation.

Transcribes meeting audio to text using Whisper AI with speaker diarization via pyannote for clear speaker identification.

Applies hierarchical clustering algorithms to group nested data structures into meaningful hierarchical clusters.

Performs Fast Fourier Transform for converting time-domain signals to frequency domain, enabling spectral analysis and decomposition.

Generates publication-quality scientific figures with journal-specific formatting using matplotlib, seaborn, and plotly.

AI-driven diagnostics for RAN systems, enabling predictive failure analysis, autonomous root cause identification, and self-healing network capabilities.