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 commands/nextlevelbuilder/ui-ux-pro-max-skill/design-plangit clone --depth 1 https://github.com/nextlevelbuilder/ui-ux-pro-max-skillWhat 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 | $0.00017 | $0.00327 |
| Opus 5 | $0.00009 | $0.00163 |
| Sonnet 5 | $0.00003 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
design-plan 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 yesterday.
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- design-plan — 100% identical, 0 lines differ
- design-plan — 100% identical, 0 lines differ
What it actually says
Before writing any markup, produce a design system for: $ARGUMENTS
-
Run the
ui-ux-pro-maxdesign-system generator to get style + color tokens + typography + UX anti-patterns:python3 <ui-ux-pro-max-skill-path>/scripts/search.py "$ARGUMENTS" --design-system -p "Project"(Resolve
<ui-ux-pro-max-skill-path>from the installed skill — see CLAUDE.md.) -
Pull anything the brief needs specifically, e.g.:
--domain color "<industry> <mood>"for the palette / semantic tokens--domain typography "<mood>"for font pairing + imports--domain web-vitals "<page type>"for the performance budget--domain ux "<pattern>"for do/don't guidance
-
Then apply the frontend-design lens: state purpose / tone / constraints / differentiation, pick ONE tone, choose a single signature element, and reject any choice that reads like a generic AI default.
Output a compact token block (4–6 named colors, 2–3 type roles, spacing scale, one signature element) and a one-paragraph rationale. Do not start building until the tokens are decided.
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.
- yesterday First seen · 27 lines · 17 tokens per session scan A bd7298174dd6
design-plan is a command published in the GitHub repository nextlevelbuilder/ui-ux-pro-max-skill (123,153 stars, last pushed 5d ago), licensed MIT. It adds 17 tokens to every session and 327 once invoked, about $0.0001 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.
Other commands, from other repositories
epic-dev
Epic/sprint development workflow with git worktrees, GitHub issues, and phased delivery.
update-rules
Update AGENTS.md/CLAUDE.md and .rules/ from latest templates.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.