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dingo-data-quality

Evaluate datasets and generated content with configurable rule-based and LLM evaluators.

1|Updated Apr 24, 2026
Search Tags:#data quality#llm evaluation#mcp server#fact checking#api key management#rag faithfulness#dataset QA
One-click install
npx skills add https://github.com/itsadijmbt/SecureMCP-Servers --skill dingo-data-quality
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dingo-data-quality
Source: https://github.com/itsadijmbt/SecureMCP-Servers/tree/main/TEST_SERVERS/PORTED_TO_SECUREMCP/dingo/clawhub
Command: npx skills add https://github.com/itsadijmbt/SecureMCP-Servers --skill dingo-data-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dingo-python, dingo-python[agent], langchain, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you evaluate the quality, completeness, safety, and factual reliability of your datasets and generated content so you can detect issues before deployment or publication.

Core Features & Use Cases

  • Run deterministic data quality checks (e.g., null/format/content rules) to catch formatting errors, missing fields, and basic quality problems quickly.
  • Run LLM-based semantic and RAG quality metrics (e.g., text quality, repeat detection, faithfulness, context precision/recall, security prohibition) to evaluate meaning, retrieval grounding, and response quality.
  • Run autonomous fact-checking on articles using ArticleFactChecker, producing an accuracy score and structured false-claim/evidence reports for review.

Quick Start

Create a config.json for your JSONL dataset and run Dingo with the command dingo eval --input config.json.

Frequently Asked Questions about dingo-data-quality

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

FAQPage Schema
How do I check data quality and detect formatting issues in a JSONL dataset?▼

To check data quality in a JSONL dataset, you can run deterministic rule-based evaluators to detect formatting issues, missing fields, and null values. This requires creating a config.json file and executing the command dingo eval --input config.json.

Can I evaluate RAG faithfulness and context precision without an API key?▼

You can evaluate RAG faithfulness and context precision using LLM-based metrics, which require an OpenAI-compatible endpoint and API key. However, basic rule-based data quality checks for formatting and content can run without any API keys.

How does fact checking work for generated articles?▼

Fact checking for generated articles uses an autonomous agent evaluator to produce an accuracy score and structured false-claim and evidence reports. This detects unsupported claims and semantic flaws before content publication.

What file formats are supported for dataset QA and content quality evaluation?▼

Dataset QA and content quality evaluation supports local JSONL, JSON, CSV, plaintext, and Parquet file inputs. You configure these inputs through a YAML-discovered skill configuration to apply customizable evaluator sets.

Do I need langchain to run LLM-based semantic quality metrics?▼

Yes, running LLM-based semantic quality metrics and autonomous fact checking requires dependencies like dingo-python[agent] and langchain. These libraries enable the agent evaluators to assess text quality, retrieval grounding, and response security.

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