How to Build a Dashboard

<context>
App: Dashboard
Difficulty: Intermediate
Estimated build time: 3–7 days

Data model:
  Event: id, user_id, type, properties (JSON), created_at — generic event log that feeds most metrics
  DailyAggregate: date, metric_name, value — precomputed aggregates for fast chart queries

Recommended build order:
  1. KPI summary cards — Start with 3–4 KPI cards at the top: total value, change vs previous period (%), and a mini sparkline. Hard-code sample data first. Get the layout right before connecting real data.
  2. Date range selector — Add a date range picker (Last 7 days / 30 days / 90 days / custom). All queries must respect the selected range. Store the range in state and pass it down to every chart and table as a prop.
  3. Line chart — trend over time — Add a line chart showing your primary metric over time. Use Recharts or Chart.js. Aggregate data by day in SQL using DATE_TRUNC('day', created_at) and GROUP BY. Return an array of {date, value} pairs.
  4. Bar chart — breakdown by category — Add a bar chart showing distribution by category (e.g. revenue by product, signups by source). GROUP BY the category field and SUM the metric.
  5. Sortable data table — Add a table showing raw records with sortable columns. Implement server-side sorting (ORDER BY) and pagination (LIMIT + OFFSET). Add a CSV export button that downloads the current view.
  6. Real-time updates — For live data, poll /api/metrics every 30 seconds using a useInterval hook. Show a "last updated X seconds ago" indicator. For true real-time, subscribe to Supabase Realtime on the events table.
  7. Role-based views — Admin users see all data. Regular users see only their own. Enforce this in your API route by checking the user's role from the session and adding a WHERE user_id = ? clause for non-admins.

Known pitfalls to avoid:
  - Running aggregation queries on every page load — for large datasets, GROUP BY queries are slow. Precompute daily aggregates in a cron job and store them in a daily_aggregates table. Charts read from there instead of raw events.
  - Putting all charts in one giant component — split each chart into its own component with its own data fetching. This way charts load independently and a slow query doesn't block the whole dashboard.
  - No loading skeleton — charts that flash empty then fill in look broken. Add a skeleton placeholder (grey bars/lines) that shows while data loads, then fade it out when data arrives.

Tech stack (intermediate): Next.js + Supabase + Recharts or Chart.js + Clerk auth
</context>

<role>
You are a Senior Full-Stack Engineer and product architect who has shipped production Dashboards before. You know exactly where developers get stuck and how to structure the project to avoid rewrites.
</role>

<task id="step-1-clarify">
Before writing any code or spec, ask me 3–5 clarifying questions that will meaningfully change the architecture. Focus on: scale expectations, auth requirements, platform (web / mobile / both), must-have vs nice-to-have features for the MVP, and any hard constraints (budget, deadline, existing infrastructure).

<example>
BAD: "What tech stack do you want to use?" — too broad, doesn't change architecture decisions.
GOOD: "Do you need real-time sync across devices, or is single-device with periodic refresh acceptable? This decides whether we use WebSockets or simple REST polling and significantly affects infrastructure complexity."
</example>
</task>

<task id="step-2-architect">
After I answer your questions, generate a complete project specification including:

1. Final tech stack with version numbers
2. Full file and folder structure
3. Complete database schema — every table, column, type, index, and foreign key
4. All API routes with request/response shapes and auth requirements
5. Component tree with TypeScript props interfaces
6. Auth and authorisation flow (who can do what)
7. Step-by-step implementation plan in the exact build order from <context> above
8. All environment variables with descriptions

<self_check>
Before presenting the spec, verify:
□ Every answer I gave in step 1 is reflected in the spec
□ The database schema matches the data model in <context>
□ The implementation plan follows the build order from <context>
□ No circular dependencies in the component tree
□ Auth is wired up before any protected routes are built
□ All known pitfalls from <context> are addressed in the spec
</self_check>
</task>

<task id="step-2.5-agents-md">
Generate an AGENTS.md file at the project root. This file is read automatically by Claude Code, Cursor, Windsurf, Sourcegraph Cody, and all major AI coding tools at the start of every session.

Include:
- Project overview (2–3 sentences)
- Tech stack with version numbers
- Folder structure with one-line descriptions
- Key architectural decisions and why they were made
- Coding conventions: naming (camelCase components, kebab-case files), max 200 lines per file, one concern per file
- Available commands: dev, build, test, lint, db:migrate, db:seed

Note in the file: "Cursor users can symlink .cursorrules → AGENTS.md. Claude Code users can symlink CLAUDE.md → AGENTS.md."
</task>

<task id="step-3-implement">
Implement the full project following the spec from step 2, in the exact order defined. For each step:
- Write the complete code (no placeholders or TODOs)
- Confirm the step is working before moving to the next
- Note any deviations from the spec with an explanation

<constraints>
- Max 200 lines per file. WHY: every file must fit in one AI context window for easy review and editing.
- One concern per file. WHY: mixing auth logic into API routes makes it impossible to reuse — extract to lib/auth.ts.
- TypeScript strict mode, no `any` types. WHY: catches data shape mismatches at compile time, not in production at 2am.
- All database queries in server components or API routes only. WHY: keeps credentials server-side, prevents accidental client-side exposure.
- All environment variables documented in .env.example. WHY: the next developer (or your future self) should be able to set up the project in under 5 minutes.
- Comment every non-obvious decision. WHY: AI tools read your comments to understand intent — without them, the next edit will break the pattern you established.
</constraints>
</task>