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 skills add Luohaothu/everything-codex --skill multi-frontendgit clone --depth 1 https://github.com/Luohaothu/everything-codexWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/luohaothu/everything-codex/multi-frontend)<a href="https://agentmods.dev/skills/luohaothu/everything-codex/multi-frontend"><img src="https://agentmods.dev/badge/skills/luohaothu/everything-codex/multi-frontend/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/luohaothu/everything-codex/multi-frontend"><img src="https://agentmods.dev/badge/skills/luohaothu/everything-codex/multi-frontend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00029 | $0.00319 |
| Opus 5.5 | $0.00012 | $0.00128 |
| Sonnet 5.5 | $0.00006 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
multi-frontend 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 1mo 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.
What it actually says
Frontend-Focused Multi-Model Workflow
Advanced skill — Frontend-focused development with Gemini-led multi-model collaboration.
Usage
/multi-frontend <UI task description>
Applicability
Component design, responsive layout, UI animations, style optimization, accessibility.
Phases
Phase 1: Research
Understand requirements, retrieve existing components, styles, design system.
Phase 2: Ideation (Gemini-Led)
- Call Gemini for UI feasibility analysis
- Output: recommended solutions (at least 2), UX evaluation
- Wait for user selection
Phase 3: Planning (Gemini-Led)
- Call Gemini for frontend architecture
- Output: component structure, UI flow, styling approach
- Save plan, wait for user approval
Phase 4: Implementation
Follow approved plan, maintain design system, ensure responsiveness and accessibility.
Phase 5: Optimization (Gemini-Led)
- Call Gemini for frontend review
- Focus: accessibility, responsiveness, performance, design consistency
- Execute optimization after user confirmation
Phase 6: Quality Review
Check completion, verify responsiveness, report issues.
Key Rules
- Gemini frontend opinions are authoritative
- Other model backend opinions are for reference only
- External models have zero filesystem write access
- All code writes handled by the orchestrator
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.
- 1mo ago First seen · 52 lines · 29 tokens per session scan A 63a61ce309a3
multi-frontend is a skill published in the GitHub repository Luohaothu/everything-codex (25 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 319 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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