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ai-enricher

Enrich data records with LLM-generated insights, classifications, and summaries.

Updated Feb 18, 2026
Search Tags:#summarization#ai#llm#data processing#classification#enrichment
One-click install
npx skills add https://github.com/Greenmamba29/skillsdotmd_web --skill ai-enricher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-enricher
Source: https://github.com/Greenmamba29/skillsdotmd_web/tree/main/.agents/skills/ai-enricher
Command: npx skills add https://github.com/Greenmamba29/skillsdotmd_web --skill ai-enricher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of adding valuable AI-generated insights, classifications, and summaries to existing data records, enhancing their utility and interpretability.

Core Features & Use Cases

  • Automated Enrichment: Batch-process records to add AI-generated metadata.
  • Customizable Prompts: Define specific enrichment tasks using flexible prompt templates.
  • Structured Output: Leverages LLM JSON mode for reliable data parsing.
  • Use Case: Enrich customer feedback records by automatically generating sentiment scores, categorizing feedback topics, and summarizing lengthy comments.

Quick Start

Use the ai-enricher skill to summarize all product descriptions in the catalog.

Frequently Asked Questions about ai-enricher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I batch process records to add LLM-generated summaries and classifications?▼

Batch processing of records to add LLM-generated summaries uses prompt templating and structured JSON output parsing. It applies to data sources like Supabase, Airtable, or CSV files to append AI-driven metadata.

Can I use prompt templates to enrich data records from Airtable or Supabase?▼

Prompt templates can enrich data records from Airtable or Supabase by defining specific LLM tasks. The skill processes these records in batches and parses structured JSON outputs to append the generated insights.

What is the best way to automatically classify customer feedback stored in a CSV file?▼

Automatically classifying customer feedback in a CSV file requires batch processing records with customizable LLM prompts. This approach generates sentiment scores, categorizes topics, and summarizes comments using structured JSON parsing.

How does structured JSON output parsing work for AI data enrichment?▼

Structured JSON output parsing for AI data enrichment works by leveraging LLM JSON mode. This ensures reliable extraction of generated insights, classifications, and summaries directly into your existing data records.

Do I need an LLM API to generate sentiment scores and categorize feedback topics?▼

Generating sentiment scores and categorizing feedback topics requires an LLM API. The skill calls the API using prompt templates to process records and returns structured JSON for reliable data parsing.

When should I not use AI data enrichment for batch processing?▼

AI data enrichment for batch processing should not be used when precise deterministic accuracy is required. LLM-generated insights and classifications may vary, making it less suitable for strict compliance record updates.

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