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 murphytrueman/design-system-ops --skill ai-component-descriptiongit clone --depth 1 https://github.com/murphytrueman/design-system-opsWrote 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/murphytrueman/design-system-ops/ai-component-description)<a href="https://agentmods.dev/skills/murphytrueman/design-system-ops/ai-component-description"><img src="https://agentmods.dev/badge/skills/murphytrueman/design-system-ops/ai-component-description/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/murphytrueman/design-system-ops/ai-component-description"><img src="https://agentmods.dev/badge/skills/murphytrueman/design-system-ops/ai-component-description.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 | $0.00065 | $0.03954 |
| Opus 5.5 | $0.00026 | $0.01582 |
| Sonnet 5.5 | $0.00013 | $0.00791 |
| Haiku 4.5 | $0.00006 | $0.00395 |
Grade A, and why
ai-component-description 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 26d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI component description
A skill for generating structured component descriptions optimised for consumption by LLMs via Figma's MCP server. Output is a six-section description that gives an AI agent the information it needs to understand, compose, and generate from a component accurately — without relying on implicit knowledge, visual inference, or team context.
Before you begin: verify references
Confirm that every path in this skill's frontmatter references: exists relative to this SKILL.md. If any is missing, stop: the install is incomplete, usually because a flattening installer (for example npx skills install) dropped the repo-root knowledge-notes/ directory. Tell the user to reinstall by a method in 1-INSTALL.md and run verify-install.sh from the install root. Proceed without the references only if the user explicitly says to, and then say in the output that it was produced without the pack's reference material.
Context
Most component descriptions are written for human designers discovering the component for the first time: "use this to show important information", "works great in cards". An LLM reading a description needs to know what the component is, what it takes, what it prohibits, how it relates to other components, and what failure modes look like. Human-readable descriptions skip most of this.
The six-section format came from watching AI agents misuse components that had perfectly fine human documentation. Each section addresses a specific class of LLM error.
Configuration
If .ds-ops-config.yml exists, follow the configuration-and-recurring knowledge note (../../knowledge-notes/configuration-and-recurring.md) for loading, integration fallbacks and recurring runs. This skill reads:
integrations.figma,integrations.storybook,integrations.githubandintegrations.documentation— which sources Step 0 can read automatically
Step 0: Check data sources and existing description
Use every source that is available:
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.
- 26d ago Changed · -11 lines c693e04d7e19
- 27d ago Changed · -87 lines · -67 tokens per session c6f71c55c64b
- 1mo ago First seen · 365 lines · 132 tokens per session scan A 22d914c2c9a1
ai-component-description is a skill published in the GitHub repository murphytrueman/design-system-ops (208 stars, last pushed 26d ago), licensed MIT. It adds 65 tokens to every session and 3,954 once invoked, about $0.0003 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.
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