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.
git clone --depth 1 https://github.com/yonatangross/orchestkitWrote 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/commands/yonatangross/orchestkit/design-context-extract)<a href="https://agentmods.dev/commands/yonatangross/orchestkit/design-context-extract"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/design-context-extract/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/commands/yonatangross/orchestkit/design-context-extract"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/design-context-extract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00084 | $0.03285 |
| Opus 5.5 | $0.00034 | $0.01314 |
| Sonnet 5.5 | $0.00017 | $0.00657 |
| Haiku 4.5 | $0.00008 | $0.00329 |
Grade A, and why
design-context-extract 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 20d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-generated from skills/design-context-extract/SKILL.md
Source: https://github.com/yonatangross/orchestkit
Design Context Extract
Extract the "Design DNA" from existing applications — colors, typography, spacing, and component patterns — and output as structured tokens.
design-context-extract /tmp/screenshot.png # From screenshot
design-context-extract /tmp/recording.mp4 # From screen recording (motion spec)
design-context-extract https://example.com # From live URL
design-context-extract current project # Scan project's existing styles
Pipeline
Input (screenshot/URL/project)
│
▼
┌──────────────────────────────┐
│ Capture │ Screenshot or fetch HTML/CSS
└──────────┬───────────────────┘
│
▼
┌──────────────────────────────┐
│ Extract │ Stitch get_screen + generate_screen_from_text
│ │ OR multimodal analysis (fallback)
│ → Colors (hex + oklch) │
│ → Typography (families, scale)│
│ → Spacing (padding, gaps) │
│ → Components (structure) │
└──────────┬───────────────────┘
│
▼
┌──────────────────────────────┐
│ Output │ Choose format:
│ → design-tokens.json (W3C) │
│ → @theme (Tailwind v4) │
│ → tokens.css (CSS variables) │
│ → Markdown spec │
└──────────────────────────────┘
Step 0: Detect Input and Context
INPUT = ""
# 1. Create main task IMMEDIATELY
TaskCreate(subject="Extract design context: {INPUT}", description="Extract design DNA", activeForm="Extracting design from {INPUT}")
# 2. Create subtasks for each phase
TaskCreate(subject="Detect input type and context", activeForm="Detecting input type") # id=2
TaskCreate(subject="Capture source material", activeForm="Capturing source") # id=3
TaskCreate(subject="Extract design tokens", activeForm="Extracting tokens") # id=4
TaskCreate(subject="Choose output format and generate", activeForm="Generating output") # id=5
TaskCreate(subject="Recommend shadcn/ui style", activeForm="Recommending style") # id=6
# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"]) # Capture needs input type detected
TaskUpdate(taskId="4", addBlockedBy=["3"]) # Extraction needs captured source
TaskUpdate(taskId="5", addBlockedBy=["4"]) # Output needs extracted tokens
TaskUpdate(taskId="6", addBlockedBy=["5"]) # Style recommendation needs output
# 4. Update status as you progress
TaskUpdate(taskId="2", status="in_progress") # When starting
TaskUpdate(taskId="2", status="completed") # When done — repeat for each subtask
# Determine input type
# "/path/to/file.png" → screenshot
# "/path/to/file.mp4|.mov|.webm|.gif" → screen recording (video pipeline)
# "http..." → URL
# "current project" → scan project styles
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.
- 20d ago Changed 1b57b4280203
- 26d ago Changed · +12 lines ad8474f1ef1d
- 27d ago Changed 4195d6140e35
- 1mo ago Changed b291dad9322a
- 1mo ago First seen · 276 lines · 84 tokens per session scan A 8428f440fd8b
design-context-extract is a command published in the GitHub repository yonatangross/orchestkit (297 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 3,285 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.
Other commands, from other repositories
pn-audit-a11y
Surgical frontend accessibility audit — WCAG contrast, keyboard navigation, ARIA roles, focus management, and screen-reader compatibility. Standalone or chained by pn-frontend-audit.
pn-audit-typography
Surgical frontend typography audit — type scale, font choices, loading strategy, hierarchy, and accessibility. Standalone or chained by pn-frontend-audit.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.