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 atezer/FMCP --skill fmcp-screen-recipesgit clone --depth 1 https://github.com/atezer/FMCPWrote 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/atezer/fmcp/fmcp-screen-recipes)<a href="https://agentmods.dev/skills/atezer/fmcp/fmcp-screen-recipes"><img src="https://agentmods.dev/badge/skills/atezer/fmcp/fmcp-screen-recipes/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/atezer/fmcp/fmcp-screen-recipes"><img src="https://agentmods.dev/badge/skills/atezer/fmcp/fmcp-screen-recipes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.07972 |
| Opus 5.5 | $0.00022 | $0.03189 |
| Sonnet 5.5 | $0.00011 | $0.01594 |
| Haiku 4.5 | $0.00006 | $0.00797 |
Grade A, and why
fmcp-screen-recipes 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 9d 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 — 647 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FMCP Screen Recipes — Fast Path Cookbook
Kullanım (v3.1+):
fmcp-screen-orchestratorAdım 0.5 Fast Path 5/5 match olduğunda çağrılır. Bağımsız giriş noktası değil. Adım 1.6 (Text Style Resolution) canonical kopyası burada — orchestrator buraya referans verir, kod duplicate etmez.
Ne Zaman Kullanılır
Fast Path DEVREYE GİRER (hepsi TRUE olmalı):
- ✅ Tek ekran üretimi
- ✅ Standart ekran tipi: 9 recipe'ten biri match ediyor
- ✅ DS tanımlı:
active-ds.mdStatus: ✅ Aktif - ✅ Platform belli
- ✅ Custom animation / prototype YOK
Devreye GİRMEZ: Multi-screen flow, custom layout, animation, explicit generate-figma-screen talebi, DS GATE geçilmemişse.
5 Mega-Adımlı Akış
Max 15 op/execute. Her mega-adım sonrası tek satır Türkçe micro-report.
M1: Pre-Flight Discovery + Token + Text Style (1 execute, ~15 op)
M2: Frame + Structure + Modes (1 execute, ~12-14 op)
M3: Component Placement (toplu, 3-4/execute) (2-3 execute, ~12-15 op each)
↳ Her M3 execute sonrası: figma_scan_ds_compliance(threshold=85) inline gate
M4: Dark Variant (1 execute, ~4 op)
M5: Validate + Final Report (ZORUNLU: figma_scan_ds_compliance(detailed) + figma_validate_screen)
Toplam execute: ~6-8 + 2-3 scan call. Hedef süre: ~10 dk. v1.9.4: Scan call'ları hızlıdır (~150ms), execute bütçesini yemezler.
3 MUTLAK KURAL
KURAL 1 — Fill Bind Zorunlu (frame + text dahil):
const paint = { type: 'SOLID', color: { r: 1, g: 1, b: 1 } };
const bound = figma.variables.setBoundVariableForPaint(paint, 'color', dsColorVar);
node.fills = [bound];
Fill panel'de variable icon 🎨 GÖRÜNMELI. Hardcoded hex YASAK. Frame fill + text fill + primitive dahil TÜM node'lar.
KURAL 2 — Variant Seçim: DEFAULT Koru:
setPropertiesile SADECE recipe'de explicit belirtilen property'leri set et- Diğer TÜM property'leri DEFAULT bırak
Product→ main (default), Boolean kontroller → recipe'de explicit yoksa DEFAULT
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.
- 9d ago Changed 82c537511951
- 1mo ago First seen · 647 lines · 55 tokens per session scan A fbb4a1c6b9e8
fmcp-screen-recipes is a skill published in the GitHub repository atezer/FMCP (10 stars, last pushed 9d ago), licensed MIT. It adds 55 tokens to every session and 7,972 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
accessibility-per-component
Audit one design system component's accessibility against WCAG 2.2 AA: keyboard, screen reader, contrast, focus, ARIA, target size. Trigger: a11y audit, is this accessible, WCAG check, screen reader support, keyboard navigation check. Not for page- or product-wide audits.
figma-variable-audit
Audit Figma variable collections: tier mapping, naming, alias chains, modes, orphans; can fix in place with Figma Console MCP. Triggers: audit my Figma variables, review variable collections, Figma variable health. For token files in code use token-audit.
theme-audit
Audit theme parity: tokens missing or unchanged per theme, component tokens bypassing semantics, contrast within each theme, resolver contexts, theme-switch regressions. Triggers: dark mode audit, theme coverage, brand variant parity. For general token structure use token-audit.
token-audit
Audit how design tokens are defined: tiers, naming, alias chains, raw values, orphans, DTCG readiness. Triggers: audit my tokens, token architecture review, token health check. Not for code consuming tokens (token-compliance), theme parity (theme-audit) or Figma variables (figma-variable-audit).
token-compliance
Find hardcoded colour, spacing and type values and wrong-tier token references in consuming code. Trigger: find hardcoded values, any hex in the code, are we using tokens correctly, token compliance. Do NOT use for token definitions — token-audit; token file format — schema-validator.
ai-component-description
Write a six-section prose description (purpose, props, anti-patterns, composition, accessibility, examples) for a Figma component's description field so LLMs read it via MCP. Triggers: describe this component for AI, Figma MCP description. JSON metadata files: use metadata-schema-generator.