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    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.

    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 use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Trust & safety

    Security scanned

    Verified clean 1 month ago

    • Passed all security checks, Safe to install

    Requires

    Python

    Listed1 month ago

    What's inside

    Frequently Asked Questions