Datus-ai
Official@datus-ai
The Future of Data Engineering: Autonomous Agents for Data Analysis
Agent Skills by Datus-ai
Showing 30 vetted skills indexed across 1 GitHub repositories.
Datus-aiosi-metrics-authoring
Authors OSI core semantic model metrics from SQL queries and natural language.
Datus-aicreate-subagent
Creates or updates custom Datus subagents by editing agentic_nodes in the loaded agent.yml configuration.
Datus-aiosi-semantic-authoring
Author OSI core schema semantic models with field roles, keys, relationships, and validation.
Datus-aidosi-semantic-authoring
Author Dosi semantic models, metrics, and window calculations as OSI YAML.
Datus-aidashboard-bootstrap
Extracts SQL and metrics from BI dashboards into reference SQL and semantic models via plugins.
Datus-aiadmin-tools
Run database health checks, table statistics refreshes, and index rebuilds.
Datus-aidata-profiler
Profile data quality metrics and value distributions for tabular datasets.
Datus-aisql-optimization
Analyze SQL execution plans and rewrite subqueries as JOINs.
Datus-aireport-generator
Generate JSON, CSV, or markdown reports from SQL query results.
Datus-aisql-analysis
Guide structured SQL data analysis with schema discovery and data sampling.
Datus-aiinit
Initialize data engineering projects with lightweight context stores and AGENTS.md skeletons.
Datus-aitable-validation
Validate database table column sets, data types, and nullability against expected contracts.
Datus-aidata-migration
Migrate tables between heterogeneous database engines with schema inspection and reconciliation.
Datus-aigen-table
Generate and execute DDL to create database tables from SQL queries or natural language.
Datus-aigen-metrics
Convert natural language metric descriptions and SQL queries into validated MetricFlow definitions.
Datus-aibuild-kb
Build vector-indexed knowledge bases from project files, database metadata, and validated SQL corpora.
Datus-aimetricflow-semantic-authoring
Generate validated MetricFlow semantic model YAML from database table schemas.
Datus-aischeduler-validation
Validate cron schedules, job configurations, and latest run statuses.
Datus-aiextract-knowledge
Extract atomic business knowledge facts from validated question and gold SQL pairs.
Datus-aisession-summarize
Harvest validated SQL, business rules, and metric definitions from chat sessions.
Datus-aicreate-skill
Generate valid SKILL.md files with YAML frontmatter for Datus skills.
Datus-aigrafana-dashboard
Create and manage Grafana dashboards with SQL-based visualization panels.
Datus-aioptimize-skill
Analyze Datus skill usage logs to identify optimization opportunities.
Datus-aibi-validation
Validate BI dashboard configurations and data accuracy for Superset and Grafana.
Frequently Asked Questions About Datus-ai
FAQPage SchemaWhat specific data engineering tasks does Datus-ai enable?▼
Datus-ai enables database health monitoring, SQL execution plan analysis, semantic model generation, and dashboard configuration validation. It supports table schema creation, cross-engine data migration, and the extraction of business logic from historical query patterns to ensure data consistency across enterprise environments.
Which technical personas benefit from these capabilities?▼
Data engineers, database administrators, and analytics engineers benefit from these capabilities. These professionals use the system to maintain data quality, manage complex semantic layers, and ensure that BI dashboards remain synchronized with underlying database schemas and metric definitions.
What are the prerequisites for implementing these data management functions?▼
Implementation requires access to relational database environments, existing SQL corpora, and configured BI platforms like Superset or Grafana. Users must provide valid database credentials and schema access to enable metadata extraction, semantic model authoring, and validation of job configurations.