Automating RAG Failure Diagnosis and Skill Creation from Articles using Loreto and Claude Code

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Skills, MCP
Original source: r/ClaudeCode post/comment

What problem this solves

Diagnosing complex RAG (Retrieval Augmented Generation) failures, particularly those involving relational or temporal queries that embeddings struggle with. It also solves the problem of operationalizing valuable insights from articles into reusable Claude Code skills, preventing them from being lost in bookmarks.

Summary

This workflow leverages Loreto, an MCP server, and Claude Code to automatically generate a reusable skill from an article. The specific example demonstrates creating a skill for diagnosing RAG failure modes (e.g., multi-hop relational, temporal sequencing) and prescribing architectural fixes (e.g., knowledge graph, timeline index). The generated skill package includes a SKILL.md with causal mechanisms and diagrams, runnable reference files (e.g., graph-query templates), and a runnable test, ensuring the skill is actionable and effective.

Why it is useful

This workflow is highly valuable because it provides a concrete, automated method for transforming external knowledge (articles) into actionable, reusable Claude Code skills. It directly addresses a common and challenging problem in RAG systems – diagnosing failures related to relational and temporal queries – by generating structured diagnoses and architectural fixes. The output is not just a summary but a fully-fledged skill package with tests and runnable content, significantly enhancing Claude's capabilities and reducing token burn by preventing repeated reasoning. This approach promotes efficient knowledge reuse and improves the reliability of AI-driven systems.

Workflow

1. Identify a valuable article or piece of literature containing actionable insights (e.g., on RAG failure modes).
2. Ensure Loreto is configured and wired in as an MCP server.
3. In your editor, instruct Claude to use Loreto to extract skills from the article URL (e.g., "Use Loreto to extract skills from <the article URL>").
4. Claude, via Loreto's generate_skills tool, processes the article.
5. Claude writes the complete, structured skill package (including SKILL.md, reference files, tests, README) directly into your .claude/skills/ directory.
6. Utilize the newly created skill within Claude Code to diagnose relevant problems or apply the prescribed solutions.

Tools / artifacts

• Loreto (MCP server)
• Claude Code
• Article URL
• .claude/skills/ directory
• Skill package (SKILL.md, reference files, runnable test, README)
• Knowledge graph query templates/schemas
• Mermaid diagrams

Validation signals

• Author states: "I hand Claude a failing query and it returns a structured failure classification + the specific architecture fix, not vibes."
• Author states: "The skill it produced isn’t a summary blob... It comes out following actual skill-authoring best practices, with the artifacts that make a skill usable instead of decorative."
• The skill package includes "a runnable test that checks concrete behavior."
• A direct link to the generated skill is provided: https://loreto.io/skills/diagnosing-rag-failure-modes

Limitations

• Requires Loreto, a specific (and potentially proprietary) tool, which might limit immediate adoption for users not already using it.
• Low community engagement on the Reddit post itself, making it harder to gauge broader interest or validation beyond the author's claims.
• The generate_skills tool is specific to Loreto; users without it would need to manually create skills or find an alternative method for automated skill generation.

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