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 alvinindra/figma-mcp-rust --skill annotation-conversiongit clone --depth 1 https://github.com/alvinindra/figma-mcp-rustWrote 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/alvinindra/figma-mcp-rust/annotation-conversion)<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.
<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>- 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 | $0.00053 | $0.00585 |
| Opus 5 | $0.00026 | $0.00293 |
| Sonnet 5 | $0.00011 | $0.00117 |
| Haiku 4.5 | $0.00005 | $0.00059 |
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.
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:
- Get selected frame/component information
- Scan and collect all annotation text nodes
- Scan target UI elements (components, instances, frames)
- Match annotations to appropriate UI elements
- 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:
-
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
-
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
-
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
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 First seen · 68 lines · 53 tokens per session scan A f53a3070a2a5
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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