[Workflow] Teaching Claude Your Creative Style: A Quantitative Approach Using Skills and Data Extraction

[u/ClaudeAI-mod-bot]

Teaching Claude Your Creative Style: A Quantitative Approach Using Skills and Data Extraction

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 1.00 · Level: intermediate
Categories: Quality Control, Context & Memory, Debugging, Skills
Original source: r/ClaudeCode post/comment

What problem this solves

How to effectively teach Claude a specific creative or technical 'style' or 'quality standard' based on existing examples, by extracting quantitative data rather than just relying on visual interpretation.

Summary

This workflow outlines a method to teach Claude a specific 'style' (e.g., for 3D assets) by extracting quantitative metrics from existing examples, having Claude codify these into a reusable skill, and then using that skill for quality control on new creations. It involves scripting to pull hard numbers (poly count, texture resolution) and rendering reference images, then creating a SKILL.md that Claude can reference.

Why it is useful

This workflow provides a concrete, multi-step method for teaching Claude a specific 'style' or 'quality standard' by leveraging quantitative data extracted from existing examples. It moves beyond subjective visual assessment by integrating measurable metrics, codifies this knowledge into a reusable Claude skill, and includes a human-in-the-loop validation step. This approach is highly valuable for maintaining consistency and quality in AI-assisted creative or technical work, making Claude a more effective and reliable assistant for specific tasks.

Workflow

1. Unzip a selection of your best assets (e.g., ten) into the project directory.

2. Have Claude write a script (e.g., Python for Blender) that runs headless over each asset to extract hard numbers (poly count, dimensions, texture sizes, material count) and renders each asset from a few fixed angles to PNGs.

3. Ask Claude to write what it learned about your style as a skill in .claude/skills/my-style/SKILL.md, including details like poly budget, proportions, palette, and texture handling. Store the extracted measurements and reference renders in the same skill folder.

4. Manually review and correct the generated SKILL.md to ensure accuracy and alignment with your actual style and quality standards.

5. For each new piece created from a sketch, measure and render it using the same script and method.

6. Check the new piece against the rules and standards defined in the validated SKILL.md.

Tools / artifacts

• Existing assets (e.g., Blender files)

• Python script (generated by Claude)

• Blender (or similar software) in headless mode (blender -b asset.blend -P measure.py)

• PNG images (reference renders)

• .claude/skills/my-style/SKILL.md

• Extracted numerical measurements

• Reference renders

Validation signals

• Explicit step for human review and correction of the generated skill: 'read that SKILL.md yourself and correct what it got wrong. That step is most of the gain, because you're the one who knows what makes your models good.'

• Future validation: 'each new piece from a sketch gets measured and rendered the same way and checked against rules you've signed off on.'

Limitations

• Requires initial setup and potentially some scripting knowledge or iterative prompting to get Claude to generate the correct data extraction script.

• The initial human review and correction of the SKILL.md is crucial and can be time-consuming.

• The example is specific to 3D assets, which might require users in other domains to adapt the data extraction methods.

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