dashboard-design

A guide for building self-contained HTML dashboards that display database information as charts and key figures.

In plain words
What is it for?
It helps create dashboards in DeepSQL, a tool for querying databases, by checking the database structure and adding the page and its charts piece by piece.
Why use it?
It helps avoid guessing about the database or producing dashboard code that cannot run against the available data.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/deepsqlai/deepsql/dashboard-design
Any agent
npx skills add DeepSQLAI/deepsql --skill dashboard-design
Clone the repo
git clone --depth 1 https://github.com/DeepSQLAI/deepsql

Made for: Claude Code, Codex.

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,273 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.05273
Opus 5 $0.00022 $0.02636
Sonnet 5 $0.00009 $0.01055
Haiku 4.5 $0.00004 $0.00527

Measured yesterday against content hash f26453d243a3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dashboard-design 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 yesterday.

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.

agent/skills/dashboard-design/SKILL.md · 294 lines

How it starts

The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dashboard Design

Use when asked to build or edit a dashboard (the task says "build a self-contained HTML dashboard"). You are a coding agent building a small, beautiful BI web app that renders inside DeepSQL — not filling in a rigid template.

The runtime you build against

Your output is a shell block, then one widget block per KPI/chart — not a single HTML document. The parent assembles them (each widget's markup+script drops into its own [data-widget=id] slot in the shell) and renders each widget into the live canvas the moment its block closes, well before your whole turn finishes — so the user watches the dashboard build piece by piece instead of staring at a blank screen for the whole generation. Both kinds of block run inside the SAME sandboxed iframe with a bridge already injected:

deepsql.connectionId                      // this connection's id (string)
await deepsql.query("SELECT ...")         // -> { columns: string[], rows: any[][] }  (read-only, access-scoped)
deepsql.ready(fn)                          // runs fn() once the bridge is live (use this to kick off loading)

// Charts — ALWAYS use these instead of hand-writing SVG. Built-in hover tooltips
// (show the value on mouse-over), number formatting, sparse axis labels, a
// graceful "No data" empty state, and a corner expand button that opens the
// same chart larger in an overlay — all automatic, nothing to wire yourself.
// Pass the deepsql.query result straight in, or [{label,value}] / [[label,value]].
// First column = label, second = value (or opts.labelKey/valueKey).
// opts: { valueFormat(fn), height, color, emptyText, title }.
deepsql.charts.bar(elOrSelector, data, opts)     // rankings, counts by day
deepsql.charts.line(elOrSelector, data, opts)     // trends over time (area+line)
deepsql.charts.donut(elOrSelector, data, opts)    // share/composition (with legend + %)
deepsql.charts.format(n)                           // human number formatter

Chart sizing and the expand control are handled by the runtime — do not build your own. Height is fixed regardless of container width (a chart in a wide card never balloons), and every chart already gets a corner "expand" button that opens a larger re-render in an overlay — this is exactly the kind of per-chart chrome that's tempting to hand-roll and easy to get inconsistent across widgets, so it's built into deepsql.charts.* once instead. Pass opts.title (the chart's plain-business-language heading) so the expanded overlay has something to show as its title — do not add your own zoom/expand/fullscreen button, modal, or lightbox; one already exists per chart.

Read the full file on GitHub · 294 lines

Changes

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.

  1. yesterday First seen · 294 lines · 43 tokens per session scan A f26453d243a3

Subscribe to this mod's changes

dashboard-design is a skill published in the GitHub repository DeepSQLAI/deepsql (23 stars, last pushed 2d ago), licensed Apache-2.0. It adds 43 tokens to every session and 5,273 once invoked, about $0.0002 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.

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