Production dashboards,
authored by your coding agent.

You don't write dashctl. Claude Code, Cursor, and Codex do. They emit a YAML spec; dashctl compiles it to one self-contained HTML file — themeable, reproducible, with live BigQuery and DuckDB. You ship it.

Set it up View on GitHub
dashctl demo dashboard Open live demo →

Live demos.

Each one is a single self-contained HTML file produced by dashctl build from a YAML spec — view-source friendly, no backend.

Northwind Commerce Polished e-commerce dashboard — KPIs, line, bar, table, with cross-filtering. Open → Sales Minimal sales overview from a single CSV — KPIs, trend, regional breakdown. Open → Filters showcase Every filter type: dropdown, multi-select, date range, slider, toggle, search. Open → DuckDB-WASM Live in-browser SQL via DuckDB-WASM — no server, no proxy, no network. Open → Hello dashctl The smallest valid dashboard — one panel, one source. The "hello world." Open →
1

Reproducible

Same prompt, same data, byte-identical dashboard. The spec lives in git, diffs are readable, regressions are obvious. Re-run the build a year later and get the same artifact — no GUI state to lose.

2

Themeable

Drop your brand colors, font, and density into one theme: block. Every dashboard your agent emits adopts it. Light, dark, or your design system — no per-chart restyling.

3

Connectable

CSV, JSON, Parquet, DuckDB-WASM in the browser, and live BigQuery via a local proxy (ADC — no OAuth dance). Snapshot for instant load or query live; same spec, your call.

The flow.

No human writes the format. You chat; the agent ships.

>_

1. You ask

"Build a sales dashboard from BigQuery — last 90 days, top regions, revenue per channel."

AI

2. Agent writes

Claude Code / Cursor / Codex emits dashboard.yaml against a published, schema-validated spec.

d/

3. dashctl builds

One command turns the spec into a single self-contained HTML file — typically < 1 MB, fully offline-capable.

↗

4. You ship

Open it, attach it to email, drop it on S3, embed it in a portal. Live BigQuery still queries from the static file.

Behind the scenes.

Yes, there is a format. Your agent writes it fluently — you don't have to look at it unless you're curious. A small, stable, schema-validated YAML keeps the agent's output predictable. 

# dashboard.yaml — what the agent writes
title: Sales
theme: light

sources:
  orders:
    type: csv
    url: ./orders.csv

panels:
  - row:
      - type: kpi
        title: Total revenue
        source: orders
        query: SELECT SUM(amount) AS [value] FROM orders
        format: currency
      - type: line
        title: Revenue over time
        source: orders
        query: SELECT date, SUM(amount) AS revenue FROM orders GROUP BY date
        x: date
        y: revenue

# in your agent / terminal
> "Build a sales dashboard from
>  ./orders.csv with revenue, top
>  customers, region breakdown."

# the agent emits dashboard.yaml, then:
$ dashctl validate dashboard.yaml
[validate] ok — 4 panels, 1 source

$ dashctl build dashboard.yaml -o dash.html
[build] wrote dash.html (842 KB)

# one file. open it, email it,
# drop it on S3.

You set it up once. The agent does the rest.

  1. npm install -g dashctl
    Node 20+. Install once on your machine; any agent that can shell out (Claude Code, Cursor, Codex, ...) can now invoke it. DuckDB-WASM is an optional dep, only pulled if used.
  2. dashctl init my-dashboard
    Drops a working dashboard.yaml + tiny CSV in the repo — a one-shot reference the agent can learn the format from. You can delete it after.
  3. Ask your agent for a dashboard.
    "Build a dashboard for ./orders.csv — revenue, top customers, region split." The agent writes the spec, runs dashctl build, hands you the HTML. That's the loop.