Vizro is an open-source Python toolkit for assembling data visualization applications from low-code configuration. It helps developers create dashboards and multi-page apps from charts, tables, controls, layouts, navigation, and interactions, with optional high-code customization. Catalogue add-ons support working with Vizro projects.
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 mckinsey/vizro --skill dashboard-buildgit clone --depth 1 https://github.com/mckinsey/vizroWrote 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/mckinsey/vizro/dashboard-build)<a href="https://agentmods.dev/skills/mckinsey/vizro/dashboard-build"><img src="https://agentmods.dev/badge/skills/mckinsey/vizro/dashboard-build/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/mckinsey/vizro/dashboard-build"><img src="https://agentmods.dev/badge/skills/mckinsey/vizro/dashboard-build.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 30 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
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 | $0.02695 |
| Opus 5 | $0.00032 | $0.01347 |
| Sonnet 5 | $0.00013 | $0.00539 |
| Haiku 4.5 | $0.00006 | $0.00269 |
Grade B, and why
dashboard-build scanned grade B with 1 finding 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 11d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
1. You MUST read the terminal to check for any errors, but do not put commands like `sleep` in it. Fix any warnings and even more important errors you encounter. ONLY once you see the dashboard running, inform the user. How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prerequisites
Requires Phase 1 spec files from the dashboard-design skill: spec/1_information_architecture.md, spec/2_interaction_ux.md, and spec/3_visual_design.md. If these do not exist, ask the user whether to run Phase 1 first or proceed without specs.
Guidelines
- Use your native tools to understand the data well, especially if you build custom charts or when you use specific selectors.
- If the user asks for an example, simply copy the example app and run it. Do not include your own data or change the example.
- When executing any script mentioned below for the first time, it may take a while to install dependencies. Plan accordingly before taking any rash actions.
- When iterating on the dashboard after completing all steps, do not forget key points from below, especially regarding spec compliance and updating and terminal handling: always keep all specs up to date, and always check if terminal output is clean after each iteration.
- Execute all scripts from this skill, and the
app.pyyou will create, withuv run <script_name>.pyoruv run app.py- this will ensure you use the correct dependencies and versions. - ABSOLUTELY NEVER type ANY commands (including
sleep,echo, or anything else) in the terminal where the dashboard app is running, even if you started it withisBackground=true. This WILL kill the dashboard process. The dashboard startup takes time - be patient and let it run undisturbed. - Step 2 (Testing) is critical — do not skip it. Use Playwright MCP if available, otherwise use any browser automation tool in your environment.
Spec Files: Documenting Decisions
IMPORTANT: Each step produces a spec file in the spec/ directory to document reasoning, enable collaboration, and allow resumption in future sessions. Create the spec/ directory if it is not already present at the root of the project.
Step 1: Build dashboard
- You MUST ALWAYS copy the example app over, and modify it - this ensures less errors!
- Investigate about the Vizro model by executing the schema fetching script. ALWAYS DO this for all models that you need - do NOT assume you know it. Execute the script like so:
uv run ./scripts/get_model_json_schema.py <model_name> <model_name2> ...where<model_name>is the name of the model you want to get the schema for (prints the full JSON schema for each model to stdout). You can get an overview of what is available by calling the overview script like so:uv run ./scripts/get_overview_vizro_models.py(prints all available model names with one-line descriptions to stdout). - Build the dashboard config by changing the copied example app. Important: Very often normal plotly express charts will not suffice as they are too simple. In that case, refer to the custom charts guide to create more complex charts. These MUST be added to the correct section in the python app. Call the custom chart function from the
Graphmodel in your dashboard app. - Run your dashboard app with
uv run <your_dashboard_app>.pyCRITICAL: After running this command, DO NOT run ANY other commands in that terminal. The dashboard takes time to start up (sometimes 10-30 seconds) - You MUST read the terminal to check for any errors, but do not put commands like
sleepin it. Fix any warnings and even more important errors you encounter. ONLY once you see the dashboard running, inform the user. NEVER run any commands in that terminal after starting the dashboard. - When you iterate, no need to kill the dashboard, as we are using debug mode. Just save the file and it will reload automatically. Check the terminal occasionally for any failures. Once failed, you need to restart the dashboard.
What ships with it
10 files 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.
- assets/4_implementation.md 592 B
- assets/5_test_report.md 714 B
- references/custom_charts_guide.md 2.7 KB
- references/data_management.md 8.6 KB
- references/examples/example_ag_grid.py 5.8 KB runs code
- references/examples/example_app.py 1.9 KB runs code
- scripts/get_model_json_schema.py 8.1 KB runs code
- scripts/get_overview_vizro_models.py 2.1 KB runs code
- scripts/validate_aggregation.py 8.6 KB runs code
- scripts/validate_colors.py 12 KB runs code
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.
- 11d ago First seen · 124 lines · 65 tokens per session scan B 70176726b0f4
dashboard-build is a skill published in the GitHub repository mckinsey/vizro (3,789 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 2,695 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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