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 text-replacementgit 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/text-replacement)<a href="https://agentmods.dev/skills/alvinindra/figma-mcp-rust/text-replacement"><img src="https://agentmods.dev/badge/skills/alvinindra/figma-mcp-rust/text-replacement.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.00770 |
| Opus 5 | $0.00028 | $0.00385 |
| Sonnet 5 | $0.00011 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
text-replacement 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 8d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent Text Replacement Strategy
1. Analyze Design & Identify Structure
- Scan text nodes to understand the overall structure of the design
- Use AI pattern recognition to identify logical groupings:
- Tables (rows, columns, headers, cells)
- Lists (items, headers, nested lists)
- Card groups (similar cards with recurring text fields)
- Forms (labels, input fields, validation text)
- Navigation (menu items, breadcrumbs)
scan_text_nodes(nodeId: "node-id") get_node(nodeId: "node-id") // optional for extra context
2. Strategic Chunking for Complex Designs
- Divide replacement tasks into logical content chunks based on design structure
- Use one of these chunking strategies that best fits the design:
- Structural Chunking: Table rows/columns, list sections, card groups
- Spatial Chunking: Top-to-bottom, left-to-right in screen areas
- Semantic Chunking: Content related to the same topic or functionality
- Component-Based Chunking: Process similar component instances together
3. Progressive Replacement with Verification
- Create a safe copy of the node before bulk replacements
- Replace text chunk by chunk with continuous progress updates
- After each chunk is processed:
- Export that section with get_screenshot for visual verification
- Verify text fits properly and maintains design integrity
- Fix issues before proceeding to the next chunk
// Clone the node to create a safe copy clone_node(nodeId: "selected-node-id", x: newX, y: newY)
// Replace text one node at a time or in batches set_text(nodeId: "node-id", text: "New text")
// Verify chunk with targeted image export get_screenshot(nodeIds: ["chunk-node-id"], format: "PNG", scale: 0.5)
4. Intelligent Handling for Table Data
- For tabular content:
- Process one row or column at a time
- Maintain alignment and spacing between cells
- Consider conditional formatting based on cell content
- Preserve header/data relationships
5. Smart Text Adaptation
- Adaptively handle text based on container constraints:
- Auto-detect space constraints and adjust text length
- Apply line breaks at appropriate linguistic points
- Maintain text hierarchy and emphasis
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.
- 8d ago First seen · 79 lines · 57 tokens per session scan A 51b76e78cd68
text-replacement is a skill published in the GitHub repository alvinindra/figma-mcp-rust (34 stars, last pushed 3d ago), licensed MIT. It adds 57 tokens to every session and 770 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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