mta-tech

On-demand time-series forecasting. CAPTURE params from project context, call run_forecast, present deterministic engine results.

Use when helping initialize, configure, or prepare a Seeknal project like a coding agent

Translate business questions into metrics, SQL evidence, and actionable recommendations

Run multi-step SQL plus Python/statistics/ML analysis while keeping tools thin and evidence grounded

Answer business questions from read-only connected databases using deterministic schema discovery and SQL evidence

Run Python code in an isolated subprocess for statistical/ML/visualization work beyond what SQL can express

End-to-end workflow for adding a new pipeline node to a seeknal project — scaffold, validate, apply, and (optionally) run via the 5 thin pipeline-build tools

Conversational data ingestion — parse files, brainstorm schema, create queryable tables, and save reusable skills

Use when analyzing revenue, sales, or financial performance. Provides standard approaches for calculating and comparing revenue metrics.

Use when checking data quality, finding anomalies, or validating data integrity. Provides patterns for identifying data issues.

An example skill demonstrating the skill file format and structure

Use this skill when creating new skills. Guides the design and implementation of skill files.

Translate business questions into metrics, SQL evidence, and actionable recommendations

Answer business questions from read-only connected databases using deterministic schema discovery and SQL evidence

Collaborative idea refinement - turns rough ideas into fully-formed designs through questioning, alternative exploration, and incremental validation. Use before writing code or implementation plans.