annotation-conversion

annotation-conversion is a skill for Claude Code from alvinindra/figma-mcp-rust. It costs 53 tokens per session (585 once invoked), scanned A, original, MIT.

A workflow for turning hand-made numbered or lettered markers in a Figma design into Figma’s built-in annotations, which attach notes directly to design elements.

In plain words
What is it for?
It helps convert annotation dots and their descriptions in a selected Figma frame or component into native Figma annotations.
Why use it?
It removes the need to recreate those notes manually and helps keep explanations connected to the correct interface elements.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the figma-mcp-rust plugin — 13 skills, 1 MCP server shipped together

Good fit It helps convert annotation dots and their descriptions in a selected Figma frame or component into native Figma annotations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alvinindra/figma-mcp-rust/annotation-conversion
Install

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.

Any agent
npx skills add alvinindra/figma-mcp-rust --skill annotation-conversion
Clone the repo
git clone --depth 1 https://github.com/alvinindra/figma-mcp-rust

Made for: Claude Code.

Or install figma-mcp-rust, the plugin that ships this one along with the rest of its 13 skills, 1 MCP server.

Wrote 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.

agentmods badge for annotation-conversion

README.md
[![agentmods](https://agentmods.dev/badge/skills/alvinindra/figma-mcp-rust/annotation-conversion/github.svg)](https://agentmods.dev/skills/alvinindra/figma-mcp-rust/annotation-conversion)
Your own site
<a href="https://agentmods.dev/skills/alvinindra/figma-mcp-rust/annotation-conversion"><img src="https://agentmods.dev/badge/skills/alvinindra/figma-mcp-rust/annotation-conversion/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.

agentmods 80×15 button for annotation-conversion

Your own site · 80×15
<a href="https://agentmods.dev/skills/alvinindra/figma-mcp-rust/annotation-conversion"><img src="https://agentmods.dev/badge/skills/alvinindra/figma-mcp-rust/annotation-conversion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 585 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.00585
Opus 5 $0.00026 $0.00293
Sonnet 5 $0.00011 $0.00117
Haiku 4.5 $0.00005 $0.00059

Measured 9d ago against content hash f53a3070a2a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

annotation-conversion 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.

skills/annotation-conversion/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Automatic Annotation Conversion

Process Overview

Convert manual annotations (numbered/alphabetical indicators with connected descriptions) to Figma's native annotations:

  1. Get selected frame/component information
  2. Scan and collect all annotation text nodes
  3. Scan target UI elements (components, instances, frames)
  4. Match annotations to appropriate UI elements
  5. Apply native Figma annotations

Step 1: Get Selection and Initial Setup

// Get the selected frame/component get_selection() // Note the selected node ID, then: get_annotations(nodeId: "selected-node-id")

Step 2: Scan Annotation Text Nodes

// Get all text nodes in the selection scan_text_nodes(nodeId: "selected-node-id")

// Filter and group annotation markers and descriptions // Markers typically have these characteristics: // - Short text content (usually single digit/letter) // - Specific font styles (often bold) // - Located in a container with "Marker" or "Dot" in the name // - Have a clear naming pattern (e.g., "1", "2", "3" or "A", "B", "C")

Step 3: Scan Target UI Elements

// Get all potential target elements that annotations might refer to scan_nodes_by_types(nodeId: "selected-node-id", types: ["COMPONENT", "INSTANCE", "FRAME"])

Step 4: Match Annotations to Targets

Match each annotation to its target UI element using these strategies in order of priority:

  1. Path-Based Matching:

    • Look at the marker's parent container name in the Figma layer hierarchy
    • Remove any "Marker:" or "Annotation:" prefixes from the parent name
    • Find UI elements that share the same parent name or have it in their path
  2. Name-Based Matching:

    • Extract key terms from the annotation description
    • Look for UI elements whose names contain these key terms
    • Particularly effective for form fields, buttons, and labeled components
  3. Proximity-Based Matching (fallback):

    • Calculate the center point of the marker using its bounds
    • Find the closest UI element by measuring distances to element centers
    • Use this method when other matching strategies fail

Read the full file on GitHub · 68 lines

Changes

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.

  1. 9d ago First seen · 68 lines · 53 tokens per session scan A f53a3070a2a5

Subscribe to this mod's changes

annotation-conversion is a skill published in the GitHub repository alvinindra/figma-mcp-rust (34 stars, last pushed 4d ago), licensed MIT. It adds 53 tokens to every session and 585 once invoked, about $0.0003 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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