natural-language-postgres
Chat app that lets you ask questions in plain English and query your PostgreSQL database.
আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Chat app that lets you ask questions in plain English and query your PostgreSQL database.
Audits Decision JSONL logs for schema compliance, required metadata, and invariants across recorded decisions.
Apache Airflow DAG development with TaskFlow API, Google Cloud operators (BigQuery, GCS), dbt integration, and dynamic DAG generation. Use when creating or modifying Airflow DAGs, implementing data pipeline orchestration, setting up cross-DAG dependencies with ExternalTaskSensor, adding deferrable operators, or configuring error handling and retries.
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.
Activate when users want to connect external data sources, cloud logs, SaaS applications, or third-party telemetry into LimaCharlie. Guides beginners through identifying requirements, obtaining credentials, and configuring adapters.
Load when working on data pipelines, datasets, reproducibility, or data infrastructure topics. Contains best practices for data engineering, ETL/ELT patterns, and ensuring reproducible data workflows.
FastAPI with PostgreSQL, async SQLAlchemy 2.0, Alembic, and Docker.
Build scalable data pipelines, ETL/ELT processes, and data infrastructure. Use when: (1) designing data architectures or lakehouse patterns, (2) building Spark/Kafka/Flink/Beam pipelines, (3) optimizing Snowflake/BigQuery/Redshift queries, (4) implementing Airflow/Prefect/Dagster orchestration, (5) setting up data quality frameworks, (6) cost-optimizing data platforms.
Deploy Supabase schema changes, manage migrations, maintain production database integrity.
Phase 1 of Ontology Builder Pipeline. Ingests and catalogs all input materials from _input/ folder. Use when starting ontology building process or when processing new input documents for domain analysis.
Convert AI fluency into throughput by embedding AI into repeatable workflows with triggers, quality gates, and iteration loops that compound over time.
Transform structured external data (CSV/JSON) into Obsidian's linked knowledge system while preserving semantic relationships, optimizing graph structure, and preventing data corruption through systematic validation and YAML-safe template generation.Enables seamless knowledge transfer from databases, spreadsheets, and APIs into personal knowledge management, maintaining referential integrity and facilitating emergence of insights through networked thought.
This skill should be used when generating detailed experimental procedures from LA-Bench format JSONL files. It orchestrates multiple subagents to parse input data, fetch reference materials, generate procedures, validate outputs, refine results, and produce final formatted outputs. Triggered by requests to process LA-Bench data or generate experimental protocols from data/public_test.jsonl or data/private_test_input.jsonl files.
Enterprise Neon Serverless PostgreSQL Platform with AI-powered database architecture, Context7 integration, and intelligent branching orchestration for scalable modern applications
Oxygen Not Included production chain calculator with SQLite database extracted from decompiled game source
Эксперт Airbyte. Используй для настройки ETL/ELT пайплайнов, коннекторов, синхронизации данных и data pipelines.
Isolate and test parser behavior on specific text snippets to debug pattern matching, validate regex patterns against edge cases, understand which extraction rules triggered, and test parser changes before full deployment without running the complete pipeline or database commit. Use this skill when: (1) Debugging why parser misinterpreted a specific line or exercise description, (2) Testing new regex patterns against edge cases before adding to parser, (3) Validating parser changes on isolated examples without full workflow, (4) Understanding which parsing rule triggered for specific input text, or (5) Developing and testing new extraction patterns in isolation
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.
Evaluate conformance of the event log to discovered models and generate deviation artefacts.