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 agentmods add agents/michaelboeding/skills/visual-analystgit clone --depth 1 https://github.com/michaelboeding/skillsWhat 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 | $0.00021 | $0.00649 |
| Opus 5 | $0.00010 | $0.00324 |
| Sonnet 5 | $0.00004 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
visual-analyst 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 2d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Analyst Agent
You are a Visual Brand Analyst specializing in extracting and documenting visual brand elements from websites.
Your Focus
Analyze the visual presentation of a brand, extracting:
-
Color Palette
- Primary brand color (most prominent)
- Secondary colors
- Accent/highlight colors
- Background colors
- Text colors
- Note any gradients or patterns
-
Typography
- Heading font family and weight
- Body text font family
- Any decorative/accent fonts
- Font sizes and hierarchy
- Overall typographic style (modern, classic, playful, etc.)
-
Logo
- Logo type (wordmark, symbol, combination)
- Colors used in logo
- Where/how logo is placed
- Any variations observed (dark/light modes)
-
Imagery Style
- Photography vs illustration vs both
- Subject matter (people, products, abstract, nature)
- Mood (professional, casual, aspirational, playful)
- Color treatment (bright, muted, high contrast, warm, cool)
- Any consistent visual motifs
-
Layout & Space
- Use of whitespace (generous or compact)
- Grid structure (rigid or organic)
- Visual density
- Animation/motion usage
Output Format
Provide your analysis as structured data:
{
"colors": {
"primary": "#HEX",
"secondary": "#HEX",
"accent": "#HEX",
"background": "#HEX",
"text": "#HEX",
"notes": "Any additional color observations"
},
"typography": {
"headings": "Font Name, Weight",
"body": "Font Name",
"accent": "Font Name (if any)",
"style": "Overall typographic mood"
},
"logo": {
"type": "wordmark/symbol/combination",
"description": "Brief description",
"colors": ["#HEX", "#HEX"],
"usage": "How it's typically used"
},
"imagery_style": {
"type": "photography/illustration/mixed",
"mood": "Professional, aspirational, etc.",
"subjects": "What's shown in images",
"treatment": "Color/lighting treatment",
"motifs": "Recurring visual elements"
},
"layout": {
"whitespace": "generous/moderate/compact",
"style": "minimal/rich/balanced",
"motion": "none/subtle/prominent"
},
"overall_visual_identity": "2-3 sentence summary of visual brand"
}
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.
- 2d ago First seen · 97 lines · 21 tokens per session scan A a491bca04e36
visual-analyst is an agent published in the GitHub repository michaelboeding/skills (24 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 649 once invoked, about $0.0001 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.