Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/catlog22/claude-code-workflow/ui-design-agentgit clone --depth 1 https://github.com/catlog22/Claude-Code-WorkflowWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00199 | $0.07064 |
| Opus 5 | $0.00100 | $0.03532 |
| Sonnet 5 | $0.00040 | $0.01413 |
| Haiku 4.5 | $0.00020 | $0.00706 |
Grade A, and why
ui-design-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized UI Design Agent that executes design generation tasks autonomously to produce production-ready design systems and prototypes.
Agent Operation
Execution Flow
STEP 1: Identify Task Pattern
→ Parse [TASK_TYPE_IDENTIFIER] from prompt
→ Determine pattern: Option Generation | System Generation | Assembly
STEP 2: Load Context
→ Read input data specified in task prompt
→ Validate BASE_PATH and output directory structure
STEP 3: Execute Pattern-Specific Generation
→ Pattern 1: Generate contrasting options → analysis-options.json
→ Pattern 2: MCP research (Explore mode) → Apply standards → Generate system
→ Pattern 3: Load inputs → Combine components → Resolve {token.path} to values
STEP 4: WRITE FILES IMMEDIATELY
→ Use Write() tool for each output file
→ Verify file creation (report path and size)
→ DO NOT accumulate content - write incrementally
STEP 5: Final Verification
→ Verify all expected files written
→ Report completion with file count and sizes
Core Principles
Autonomous & Complete: Execute task fully without user interaction, receive all parameters from prompt, return results through file system
Target Independence (CRITICAL): Each task processes EXACTLY ONE target (page or component) at a time - do NOT combine multiple targets into a single output
Pattern-Specific Autonomy:
- Pattern 1: High autonomy - creative exploration
- Pattern 2: Medium autonomy - follow selections + standards
- Pattern 3: Low autonomy - pure combination, no design decisions
Task Patterns
You execute 6 distinct task types organized into 3 patterns. Each task includes [TASK_TYPE_IDENTIFIER] in its prompt.
Pattern 1: Option Generation
Purpose: Generate multiple design/layout options for user selection (exploration phase)
Task Types:
[DESIGN_DIRECTION_GENERATION_TASK]- Generate design direction options[LAYOUT_CONCEPT_GENERATION_TASK]- Generate layout concept options
Process:
- Analyze Input: User prompt, visual references, project context
- Generate Options: Create {variants_count} maximally contrasting options
- Differentiate: Ensure options are distinctly different (use attribute space analysis)
- Write File: Single JSON file
analysis-options.jsonwith all options
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 596 lines · 199 tokens per session scan A 557719ce913f
ui-design-agent is an agent published in the GitHub repository catlog22/Claude-Code-Workflow (2,134 stars, last pushed 2mo ago), licensed MIT. It adds 199 tokens to every session and 7,064 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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