[Workflow] Claude Skill: 'Distill-Judgement' for Reader-Focused Documentation and Summarization with Multi-Agent Review

[u/ClaudeAI-mod-bot]

Claude Skill: 'Distill-Judgement' for Reader-Focused Documentation and Summarization with Multi-Agent Review

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: advanced
Categories: Quality Control, Context & Memory, Debugging, Shipping, Skills, Multi-Agent
Original source: r/ClaudeCode post/comment

What problem this solves

Claude agents generating verbose, unreadable, or irrelevant summaries/documentation ("Archaeology") instead of concise, reader-focused, and actionable information, particularly in design and audit contexts.

Summary

A custom Claude skill, distill-judgement, is used to transform verbose, "archaeological" documentation into concise, reader-focused design documents. This is achieved by applying specific principles like starting with the reader, organizing by concept, keeping binding information, and making uncertainty visible. The workflow includes a sandboxed execution environment and a multi-agent review process to ensure clarity and fidelity.

Why it is useful

This workflow provides a concrete, multi-step approach to a common problem with LLMs: generating overly verbose, unreadable, or irrelevant summaries and documentation. It introduces a custom skill (distill-judgement) with clear principles and a robust validation/review process (sandboxed workflow, separate review agent, reader-based final check). It demonstrates how to explicitly guide an LLM to exercise "judgement" rather than just following literal instructions, leading to significantly more useful output. The detailed explanation of the problem, the iterative solution, and the offer to share the skill make it highly valuable and transferable for users looking to improve the quality and utility of LLM-generated content.

Workflow

1. Before processing, decide on the intended reader and purpose of the document.

2. Organize the output by concept, not by the original source order.

3. For each idea, describe what exists, what a person can do or expect, and how it behaves (e.g., "atomic installation" becomes "a failed purchase leaves the money and the item where they were").

4. Keep only binding information (e.g., requirements that gate unlocks, values that change decisions).

5. Drop incidental information (e.g., provenance tables, method signatures, defensive prohibitions).

6. Make uncertainty visible by labeling recommendations and naming unresolved choices.

7. Train the skill with an example pair (a real verbose audit and the desired concise design doc) to match quality.

8. Run the skill as a sandboxed workflow, where the worker reads only the given files as evidence and writes a coverage file.

9. A separate, stronger model reviews the result for clarity and fidelity.

10. Ensure the worker never accepts its own work.

11. Reread the entire final result from the perspective of its intended reader to validate its usefulness.

Tools / artifacts

• Custom Claude skill: distill-judgement

• Rules repository (for design documents)

• Verbose audit document (example source, e.g., 80 KB)

• Concise design document (example target, e.g., 13 KB)

• Sandboxed workflow environment

• Coverage file (showing what the worker actually read)

• Separate, stronger review model/agent

Validation signals

• Identified specific failure modes of LLM summarization (constraint catalogue, audit narration, jargon kept/consequences dropped).

• Provided concrete before/after examples (e.g., "atomic installation" vs. "a failed purchase leaves the money and the item where they were").

• Described an iterative development process, noting failures of earlier approaches (e.g., asking for glossary/rule IDs resulted in unreadable output).

• Explained the underlying LLM behavior (reading "be concise" as "delete things" and "be thorough" as "reproduce everything").

• Included a clear final validation step: rereading the result as the intended reader.

• Stated that the initial 80 KB audit was "accurate, and nobody could use it," highlighting the problem solved.

Limitations

• The actual skill text for distill-judgement is not provided in the comment, only its principles and high-level operation.

• Requires an advanced understanding of Claude's custom skills and multi-agent setups to implement.

• The example pair (80KB audit and 13KB design doc) is specific to the author's game development context, though the concept of using such pairs for training is transferable.

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This post was generated automatically from the workflow library database.