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Graphrag Evaluation

Checked
lyndonkl·vlatest·ai-ml

Evaluates generated graphs for correctness, clarity, and adherence to specified criteria – boosting data visualization quality & reliability.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add lyndonkl/claude --skill graphrag-evaluation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

lyndonkl

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned lyndonkl/claude at 4acc337d4d.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
4acc337d4d
Manifest
skills/graphrag-evaluation/SKILL.md
Scanned
4 files, 836 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill, graphrag-evaluation, provides a mechanism to evaluate Retrieval Augmented Generation (RAG) pipelines. It assesses the quality of retrieved documents and their relevance to user queries within a RAG system. The evaluation process helps identify areas for improvement in both retrieval and generation components.

When to use it

  • Debugging RAG performance: Use this skill when your RAG application is producing inaccurate or irrelevant responses.
  • Optimizing Retrieval Strategies: Evaluate different retrieval methods (e.g., keyword search, semantic search) to determine which yields the best results for specific query types.
  • Improving Document Relevance: Assess whether the retrieved documents are truly relevant to the user's question and contributing to a helpful answer.
  • Benchmarking RAG Systems: Compare the performance of different RAG configurations or models using standardized evaluation metrics.

Key capabilities

  • Evaluates RAG pipelines.
  • Assesses quality of retrieved documents.
  • Determines relevance of retrieved documents to user queries.
  • Identifies areas for improvement in retrieval and generation components.

Example prompts

  • "Evaluate the RAG pipeline's response to this query: 'What are the symptoms of a common cold?' using these retrieved documents..."
  • "Assess the relevance of these documents to the question, 'How does photosynthesis work?'"
  • "Can you score the quality and relevance of the following document in relation to the prompt, 'Explain the concept of blockchain'?"

Tips & gotchas

The skill’s effectiveness relies on providing it with both the user query and the retrieved documents for evaluation. Ensure the documents are properly formatted or structured for optimal analysis.