Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Data Pipeline Agent

    1

    Turn any API or CSV into a ready-to-query SQLite database in minutes. Zero-config schema inference, type validation, and optional RAG vectors.

    $12

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    data-pipeline-agent

    data-pipeline-agent

    Example session with this skill installed

    I need to sync my product catalog from a CSV file at './imports/products.csv' into a SQLite db. I also need embeddings for the 'description' column using OpenAI.

    • Read your context and instructions
    • Compiled the data-pipeline-agent
    • Generated the data export

    I have configured the data pipeline for your product catalog. I created the necessary configuration file, inferred the schema from your CSV, and prepared the ingestion scripts.

    Data Pipeline Configuration

    source:
  type: csv
  path: ./imports/products.csv

destination:
  database: ./data/inventory.db
  table: products

embeddings:
  enabled: true
  model: openai
  columns: [description]

sync:
  mode: incremental
  key_column: sku
  updated_at_column: last_modified

    I have also generated the schema.json by inspecting the CSV headers and data types.

    Execution Commands:

    1. Run ingestion: python scripts/ingest.py --config config.yaml --schema schema.json
    2. Generate embeddings: python scripts/embed.py --config config.yaml --database ./data/inventory.db

    Next steps

    • Add your OPENAI_API_KEY to the .env file.
    • Run the ingestion script to populate inventory.db.
    • Execute the embedding script to build the vector search table.

    data-pipeline-agent.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Automate data ingestion from CSVs or APIs into a structured SQLite database.Generate and sync vector embeddings for RAG-based AI applications.Handle API rate limits and retries for resilient data fetching.Maintain incremental syncs between external sources and local agent memory.

    About this skill

    The problem

    Local data pipelines for RAG or analytics become brittle scripts that fail on schema changes. You waste time on type validation, sync, and embeddings boilerplate.

    What it does

    • Ingests APIs, CSVs, and Google Sheets into SQLite
    • Auto-infers schema and validates types
    • Incremental sync via primary keys and updated_at
    • Optional embeddings for text columns (local or OpenAI)
    • Retry with exponential backoff

    Why it beats prompting yourself

    Generic prompts miss edge cases: database locking, rate limiting, schema evolution. Ships dedicated scripts for inference, ingestion, validation.

    Use cases

    • Sync CRM or CMS into SQLite for RAG
    • Self-updating analytics dashboard from CSV exports
    • Local knowledge base for AI agents with auto-embeddings
    • ETL prototype without heavyweight tools

    Limitations

    Not for real-time streaming, cloud warehouses, or binary data.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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