Claude for Design Work: How to Stop Repeating the Same Taste Arguments Every Session
Running a web agency on Claude (Cursor, Claude Code, custom skills) produces solid code fast, but design taste degrades across sessions. The root cause: Claude has no persistent memory of rejected variants, accepted tokens, or design judgments. Brand facts are preserved; taste history is not.
The Core Problem
- A Stripe-inspired hero brief (brand docs, screenshots, 6K-token system prompt) generated gradient mesh, glassmorphic cards, feature pills, glow CTAs — generic 2023 SaaS #4 output. It took 18 iterations to fix, and spacing tweaks in prompt 13 reintroduced the glassmorphic card killed in prompt 5.
- A premium law firm brand kit produced fintech-style rounded cards and soft blue because Claude's training distribution averages “professional + clean” to that look.
- Two sections from the same kit (hero vs testimonials) in fresh sessions produced mismatched type pairings and button styling — as if designed by different studios.
Why Prompts and Projects Don't Fix It
Prompts forget. CLAUDE.md stores facts, not judgments. Projects pin documents, not taste history. The accumulated decisions (“8px radius forever,” “no glassmorphism on this account”) vanish each session.
The Workaround (Custom Memory Layer)
The author wired a separate layer that holds:
- Rejected variants
- Accepted tokens
- Voice rules with examples of what the client actually shipped
This subset is injected into every Claude call as live context. Same model, but now reading taste history instead of guessing.
Open Questions
The author asks the community:
- How do you keep taste consistent across sessions in AI-driven design work?
- Has anyone gotten Projects +
CLAUDE.mdto persist judgments (not just facts)? - Anyone using a memory layer (custom or off the shelf) specifically for design work?
📖 Read the full source: r/ClaudeAI
👀 See Also
Tandem MCP: Run and Manage Claude Code Sessions from Claude.ai Chat
Tandem is an open-source MCP server that links Claude.ai chat to local Claude Code sessions, allowing autonomous coding loops without copy-pasting.
Reflect MCP Server Implements Reflexion Paper for Persistent Coding Agent Memory
A developer implemented the Reflexion paper (Shinn et al., NeurIPS 2023) as an MCP server to give local coding agents persistent memory of their mistakes. The system uses regex-based pattern matching on error messages and stores lessons in SQLite with FTS5.
Layerkit: AI Image Editor with Editable Layers Built with Claude Code
A developer built Layerkit, a browser-based AI image editor that generates scenes with editable layers to avoid constant re-prompting. The tool uses a multi-stage AI pipeline where one LLM plans composition, an image model generates the scene, and another LLM analyzes the actual image to place readable text.
SkillOpt: Optimizing Markdown Skill Files as Trainable Parameters for AI Agents
SkillOpt formalizes the ad-hoc process of editing markdown skill files for AI coding agents, using frontier models to propose bounded edits gated against validation sets. Best skills converge with 1-4 accepted edits out of many proposals, and transfer across models like Codex to Claude Code.