Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/majiayu000/claude-skill-registrynpx agentmods add skills/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skillsWrote 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/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skills)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skills"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skills/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/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skills"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-canvas-edrouhardmicrosoft-agent-canvas-skills.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 | $0.00083 | $0.01993 |
| Opus 5 | $0.00042 | $0.00996 |
| Sonnet 5 | $0.00017 | $0.00399 |
| Haiku 4.5 | $0.00008 | $0.00199 |
Grade A, and why
agent-canvas 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 9d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Canvas
Interactive element picker that opens a browser window with a DevTools-like selection overlay. Users hover to highlight elements and click to select. Returns detailed element info including selector, bounding box, and computed styles.
First-Time Setup
Before first use, verify dependencies are installed:
uv run .claude/skills/agent-canvas-setup/scripts/check_setup.py check
If checks fail, ask user which installation scope they prefer and run:
# Recommended: minimal footprint, uv manages deps on-demand
uv run .claude/skills/agent-canvas-setup/scripts/check_setup.py install --scope temporary
# Alternative: create .venv in project
uv run .claude/skills/agent-canvas-setup/scripts/check_setup.py install --scope local
See agent-canvas-setup skill for full details on installation options.
Quick Start for AI Agents
When using agent-canvas, always follow this pattern:
- Launch the picker (browser opens for user interaction)
- Wait for browser to close (user finishes selecting/editing)
- Read session from disk (NOT from stdout - it may be lost)
# 1. Launch (user interacts with browser)
uv run .claude/skills/agent-canvas/scripts/agent_canvas.py pick http://localhost:3000 --with-edit --with-eyes
# 2. After browser closes, read the latest session
SESSION_ID=$(ls -t .canvas/sessions/ | head -1)
cat .canvas/sessions/$SESSION_ID/session.json | jq '.summary'
Commands
SKILL_DIR=".claude/skills/agent-canvas/scripts"
Pick Element
Open browser with element picker overlay. Streams selection events as JSON lines until window is closed:
# Basic pick - opens browser, streams selections as JSON lines
uv run $SKILL_DIR/agent_canvas.py pick http://localhost:3000
# Pick with agent-eyes integration (adds screenshot + detailed styles per selection)
uv run $SKILL_DIR/agent_canvas.py pick http://localhost:3000 --with-eyes
# Pick with edit panel (floating DevTools for live style editing)
uv run $SKILL_DIR/agent_canvas.py pick http://localhost:3000 --with-edit
# Full workflow: picker + edit panel + agent-eyes (recommended)
uv run $SKILL_DIR/agent_canvas.py pick http://localhost:3000 --with-edit --with-eyes
# Save all selections and edits to file when done
uv run $SKILL_DIR/agent_canvas.py pick http://localhost:3000 --with-edit --output ./session.json
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 222 lines · 83 tokens per session scan A 1efacd2b4f42
agent-canvas is a skill published in the GitHub repository majiayu000/claude-skill-registry (604 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 1,993 once invoked, about $0.0004 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 2026-09-03.
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refactoring-ui
Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent…