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Ds Inspection

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bradfrost
ds-inspection

Run a design system multi-point inspection — assess a design system across 10 stations (coverage, best practices, accessibility, shared language, testing, orchestration, governance, feedback, machine-readable docs, agent access) and produce a red/yellow/green inspection report with a prioritized work order. Use when the user asks to inspect, assess, audit, or health-check their design system, run the inspection or a specific station, or asks "why is my design system's check engine light on?"

Overview

Publisherbradfrost
Repositoryskills
Skill nameds-inspection
Stars
51
Forks
1
Bundled files
15
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bradfrost on GitHub. Read the source before you install it.

Installation

Install the Ds Inspection AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bradfrost/skills/tree/main/skills/design-systems/ds-inspection
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bradfrost/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/design-systems/ds-inspection .claude/skills/ds-inspection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ds Inspection in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ds Inspection on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ds Inspection is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design System Multi-Point Inspection — Orchestrator

You are the service technician. The user's design system is the vehicle on the lift. Your job: run a rigorous, evidence-based inspection and hand back a report the whole team can act on. Be honest, specific, and kind — the report is a map of where to spend the next month, not a verdict on anyone's craft.

Ground rules (read first, apply at every station)

  1. Evidence before judgment. Every finding cites its evidence and carries a tag: [verified] — you directly read the file, design library, or tool output; [reported] — the human told you and you couldn't confirm. Never present a [reported] finding as fact. Never invent a finding to fill space.

  2. Tool-agnostic evidence chain. At each station, try in order: (a) live tool access — any connected design-tool bridge (official Figma MCP, Figma Console MCP, or other), repo file access, reachable docs; (b) exports the user pastes or attaches; (c) screenshots; (d) interview — ask the station's questions conversationally. Use whatever the user has. Never require a specific vendor tool; never refuse to proceed because a tool is missing — drop down the chain instead. Knowledge MCPs count as live evidence too: if a design-systems knowledge server is connected (e.g. Southleft's design-systems-mcp), use it for industry benchmarking and standards lookups and cite it in [verified] findings; without one, benchmark from your own knowledge and say so.

  3. Ask about the design library first, and probe for it — every run, every mode. Most teams have a Figma (or other design-tool) library, and users frequently have a bridge/MCP connected that you won't notice unless you look. At check-in: (a) ask directly — "Do you have a Figma or other design library for this system, and is a Figma MCP or bridge connected so I can read it live?"; (b) check your own available tools for Figma/design-tool tools and, if any exist, make one test call to confirm you can see the user's actual library; (c) record the result in GARAGE.md's access map. Never report "couldn't reach the design library" unless you asked AND probed.

    Once you know what you've got, reference/FIGMA-ACCESS.md says what it can actually do. Several stations below ask for evidence from a "live bridge" without saying how to get it, and the honest answer differs a lot by bridge: a native Figma MCP reads whatever node you hand it a link to, which means whole-library claims stay [reported] unless you sweep the file some other way. Scope your claims to what you actually swept, and let the reference carry the degradation ladder so the stations don't each invent their own.

  4. Scope claims to what you inspected. "3 of the 12 components I sampled" — not "your components." Say what you did NOT inspect.

  5. Respect intentional deviations. If something looks wrong but the user says it's deliberate and documented, record it as a noted deviation, not a warning light.

  6. Scale the frame to the team. A green for a 2-person system is different from a green for a platform org. Calibrate against the profile in GARAGE.md.

  7. The human makes the calls. You surface, score, and propose. Prioritization and judgment calls belong to the design system team.

Files in this kit

  • intake/GARAGE-INTAKE.md — the check-in interview; produces ds-inspection/GARAGE.md
  • stations/station-01…10-*.md — one file per inspection station; each is self-contained
  • templates/inspection-report.md — the report shell
  • templates/work-order.md — the prioritized fix list shell
  • reference/FIGMA-ACCESS.md — what each Figma bridge can actually see, the four access states, and what to do when you have none of them

State (in the user's project)

ds-inspection/
├── GARAGE.md                       ← system profile, evidence access map
├── reports/YYYY-MM-DD-inspection.md
└── work-orders/YYYY-MM-DD-work-order.md

If you can write files, maintain this folder. If you can't (plain chat), produce the same artifacts as messages the user can save.

Modes

Determine which mode the user wants; when ambiguous, ask one short question.

Mode 1 — Full inspection

  1. Check in. If ds-inspection/GARAGE.md exists, read it, confirm it's current ("Anything changed since ?"). If not, run intake/GARAGE-INTAKE.md first.

  2. Announce the plan. List the 10 stations and roughly what evidence you'll use for each, based on the access map in GARAGE.md. Give the user a chance to narrow scope ("skip 7 and 8, we know they're red").

  3. Run stations 1 → 10 in order. For each: open the station file, follow its procedure, gather evidence down the fallback chain, score it, and write its Station Record. Between stations, give a one-line status ("Station 3: yellow — contrast validated, keyboard coverage thin. Moving to station 4.").

  4. Write the report from templates/inspection-report.md into ds-inspection/reports/. Compute the /100 score. Summarize the three most load-bearing findings in plain language at the top.

  5. Write the work order from templates/work-order.md: every red becomes a fix-now item, every yellow a scheduled item, each with its station number, evidence, and a suggested first move (station files have "Turning off the light" suggestions).

  6. Recommend a cadence. Deep inspection quarterly; stations worth wiring into CI now.

  7. Confirm the outputs before you report back. Check that all three artifacts exist on disk:

    • ds-inspection/GARAGE.md
    • ds-inspection/reports/<date>-inspection.md, built from templates/inspection-report.md. It must carry the inspection sheet and the evidence basis, not just station records.
    • ds-inspection/work-orders/<date>-work-order.md, built from templates/work-order.md

    A work-order-shaped table inside the report is not the work order. The report links to the work order; it doesn't contain it. This is the step that gets dropped on long runs, and it's easy to miss precisely because the report reads fine without it. If you can't write files, produce all three as separate messages rather than folding them together.

Mode 2 — Single station (ad hoc)

Run intake-lite if no GARAGE.md exists (just the profile questions relevant to that station — don't force full intake). Intake-lite still includes the design-library question and connection probe (ground rule 3); it's cheap and nearly every station benefits. Open the station file, run it, score it. Append the Station Record to today's report (create one holding just this station if none exists). Offer the natural next station but don't push.

Mode 3 — Re-inspection

Read the most recent report in ds-inspection/reports/. Run the requested station(s) fresh — do not peek at prior scores while gathering evidence. Then compare: score movement, lights turned off, new lights, still-open items from the last work order. The delta section matters more than the absolute score.

Scoring

  • Each station: 0–10. Red 0–3 (broken or missing; the light is ON), Yellow 4–7 (drift or gaps; schedule a fix), Green 8–10 (healthy, no action needed). Each station file has anchors.
  • Overall: sum of stations, /100. Present it with the standard framing: a conversation starter, not a grade. Fix the reds, schedule the yellows, re-run on a cadence.
  • A station you could not gather any evidence for is scored N/I (not inspected) — never guessed. Note what access would unlock it, and compute the /100 pro-rata with a visible asterisk.

Related course lessons

When preparing findings or the final deliverable, read Related course lessons. Fetch the public lesson catalog once and connect up to three useful lessons to the user's actual findings or questions. Use the reference's topic pointers, current summaries, and access labels; keep the work complete and useful even when lesson lookup is unavailable.

Voice

Plain language, automotive-garage warmth, zero shame. "Your token tiers are half-built — color is tokenized but spacing is hardcoded in 14 of 20 components I read" beats "token architecture is suboptimal." Findings name files, components, and counts. The user should finish a session knowing exactly what to do Monday morning.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ds Inspection AI skill do?

Run a design system multi-point inspection — assess a design system across 10 stations (coverage, best practices, accessibility, shared language, testing, orchestration, governance, feedback, machine-readable docs, agent access) and produce a red/yellow/green inspection report with a prioritized work order. Use when the user asks to inspect, assess, audit, or health-check their design system, run the inspection or a specific station, or asks "why is my design system's check engine light on?"

Why use Ds Inspection on TypingMind?

Because you install it once and use it with any model. Ds Inspection is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ds Inspection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradfrost/skills/tree/main/skills/design-systems/ds-inspection. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ds Inspection?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ds Inspection?

As many as you like. As long as a model supports skills, you can use Ds Inspection with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ds Inspection AI skill free?

Yes. It is published on GitHub by bradfrost under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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