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 theam/claude-dev-kit --skill figma-fetchgit clone --depth 1 https://github.com/theam/claude-dev-kitWrote 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/theam/claude-dev-kit/figma-fetch)<a href="https://agentmods.dev/skills/theam/claude-dev-kit/figma-fetch"><img src="https://agentmods.dev/badge/skills/theam/claude-dev-kit/figma-fetch/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/theam/claude-dev-kit/figma-fetch"><img src="https://agentmods.dev/badge/skills/theam/claude-dev-kit/figma-fetch.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 | $0.00040 | $0.00439 |
| Opus 5.5 | $0.00016 | $0.00176 |
| Sonnet 5.5 | $0.00008 | $0.00088 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
figma-fetch 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
Figma Fetch
Pull design context from Figma so the implementation matches the intended UI.
Trigger
Any URL matching figma.com/(design|file)/([A-Za-z0-9]+).
How to parse the URL
Given https://www.figma.com/design/ABcd1234EFgh/My-File?node-id=123-456:
- File key: segment after
/design/or/file/→ABcd1234EFgh - Node ID: the
node-idquery param with-replaced by:→123:456
How to fetch
Use the Figma MCP tools to read the file/node. Walk the node tree; collect TEXT node content and the name/type of every named node, preserving nesting depth.
If the MCP server is not authenticated, tell the user to authorize the Figma connector (via /mcp or their claude.ai connector settings) and stop. Do not invent design content.
Output format
- Node hierarchy with all text content, indented by depth:
===== Figma Node: <name> (<type>) =====
[FRAME] Screen name
[TEXT] Visible label
[INSTANCE] ComponentName
[TEXT] Button copy
- Plain-English UI summary: what screens/components are shown, what labels exist, and the apparent layout intent.
- Designer notes: call out any text layers that read as annotations rather than UI copy.
After fetching
Incorporate the Figma context into the implementation plan before presenting it to the user. When implementing:
- Reuse existing project components first, then the project's UI library, and only then create new components.
- Never use inline styles; follow the project's styling conventions (see the consuming repo's CLAUDE.md).
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 · 48 lines · 40 tokens per session scan A 04d854a6a7fc
figma-fetch is a skill published in the GitHub repository theam/claude-dev-kit (15 stars, last pushed 22d ago), licensed Apache-2.0. It adds 40 tokens to every session and 439 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 2026-08-30.
Other skills, from other repositories
review-all
Multi-agent code review for diffs (project-agnostic). Covers standards, bugs, security, DRY, smells, perf, tests, API contracts, a11y/i18n. Verifies each finding to eliminate false positives. Use for /review-all, pre-PR/pre-commit review, or auditing uncommitted/staged changes.
a11y-axe-playwright
Write and review automated axe accessibility scans using @axe-core/playwright: setup, tag selection, selector scoping, interpreting violations, and waiving known rules.
aesthetic-instrument
A committed aesthetic in the sense of skills/committed-aesthetic/SKILL.md: every rule below is checkable, and every value was read out of the source — packages/board/public/index.html and greatcto-site/styles.css — rather than chosen to write this document.
committed-aesthetic
How to write — and how to use — a skill that IS one aesthetic rather than a catalogue of them. A catalogue lets an agent pick, and it picks the modal option; a committed aesthetic makes it execute one thing precisely, against rules you can check. Use when a design keeps coming out competent and forgettable, when…
meta-prompting
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes"…
review-prompt
Review LLM prompts against the prompt-engineering skill's principles — leading with where each line came from — and report the findings without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable.