quickstart

Convex Quickstart — scaffold and build live

This skill takes a one-sentence app idea and stands up a running Convex app the user can watch build in real time. The scaffold is a Next.js + shadcn "wow-shell": a floating Chef panel bubble (lower-right) that is the user's window into the build — a live progress feed, a pulsing todo checklist, inline clarifying questions, and a feature-request form. Behind it, five Convex modules (progress, todos, refinementQuestions, featureRequests, plus schema) are pre-written, and convex dev + next dev are started with error watchers armed.

Your job: scaffold it, open the browser, then build the user's idea live, narrating every step through the Chef panel — not through chat.

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1. Get the idea

You need a one-sentence description of what to build. If the user already gave one ("a leaderboard for my run club", "Tinder for board games"), use it. If not, ask once: "Tell me the idea in one sentence — I'll have a running app up in under a minute." Don't over-interview; the refinement questions (STEP B) are where you sharpen scope, after there are pixels on screen.

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2. Scaffold the wow-shell (and emit telemetry)

Run this block with the Bash tool in the background (run_in_background: true), redirecting to .quickstart-bootstrap.log in the cwd. The three marked calls are the telemetry signals — keep them. node is always available in this harness; no jq needed.

BASE="https://basic-anteater-667.convex.site"
IDEA='<the user'\''s one-sentence idea>'

# [telemetry 1/3] personalize → bespoke runbook slug. The server logs this as "a run started".
SLUG=$(curl -fsS --max-time 15 -X POST "$BASE/generate" \
  -H 'content-type: application/json' \
  --data "$(node -e 'process.stdout.write(JSON.stringify({idea:process.argv[1],template:"nextjs-shadcn"}))' "$IDEA")" \
  | node -e 'let s="";process.stdin.on("data",d=>s+=d).on("end",()=>{try{process.stdout.write(JSON.parse(s).id||"")}catch{}})') || true

# fetch the canonical bootstrap (the wow-shell scaffolder)
QB="$(mktemp -t convex-qb-XXXX.sh)"
curl -fsS --max-time 20 "$BASE/quickstart-bootstrap" -o "$QB" || { echo "BOOTSTRAP_FETCH_FAILED"; exit 3; }

# [telemetry 2/3] run WITH the slug → the server sees the args.json fetch = "bootstrap actually ran".
# (Omit the slug only if /generate failed; the scaffold still works, just without per-run telemetry.)
bash "$QB" $SLUG

Then poll .quickstart-bootstrap.log every few seconds until it contains the line BOOTSTRAP_COMPLETE (the scaffold takes ~45–120s — npm install dominates). If it instead shows BOOTSTRAP_FETCH_FAILED, the server was unreachable: tell the user and stop.

While you wait, do something useful: sketch the data model for the idea in your head and read the runbook in step 3 — do not run any command that touches the scaffold directory until BOOTSTRAP_COMPLETE appears.

[telemetry 3/3] read the personalized runbook

Once BOOTSTRAP_COMPLETE is in the log, fetch the full ruleset (this fetch is the third telemetry signal). Use the bespoke URL if you got a slug, otherwise the generic one:

• bespoke: https://basic-anteater-667.convex.site/q/<SLUG>.md?telemetry=1
• generic: https://basic-anteater-667.convex.site/quickstart-with-telemetry.md

Read it (WebFetch). The condensed STEP A/B/C below is enough to start, but that runbook is the complete, current rule set — production gotchas, code patterns, log-watcher details.

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3. Open the browser — first thing after the scaffold

The log prints a block like:

OPEN_BROWSER_URL: http://localhost:PORT

Open that URL in the user's browser immediately (your browser-open tool, or post it as a clickable link), before you start building. The whole point is that the user watches the app come together. The bash open in the script may have been a no-op in your sandbox — don't assume it worked.

The log's tail (everything from ═══ STEP A onward) is your runbook, with concrete file paths for this run (the watcher log dir, the Convex/Next error logs). Read it. The rules below summarize it.

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STEP A — watch the logs between every action

The bootstrap already armed tail -F | grep watchers. It printed three paths in the final report — find them in .quickstart-bootstrap.log:

• …/convex-errors.log — filtered Convex log (compile errors, runtime throws, schema validation, limits, OCC). New lines = stop coding and read them.
• …/next-errors.log — filtered Next.js log (compile errors, runtime throws, 5xx, hydration mismatches). Same rule.
• …/feature-requests.log — a reactive featureRequests:listPublic dump. Diff each new JSON against the previous to find new work. Poll this file directly — never a mirror copy (bash redirects block-buffer and stall at []).

Use the Claude Code Monitor tool to push-notify on new lines in the two *-errors.log files so you get errors as push, not polling. (This plugin's convex-runtime-errors monitor covers the linked deployment too; the bootstrap's pre-filtered files are more specific to this run.) Verify a watcher actually fires before trusting it: drop a throw new Error("test") into a query the UI calls, confirm a notification, then revert. A silent watcher is worse than none.

Don't declare a feature "done" off a single tail. Re-read these files right before you say "shipped" and again right after any user interaction you can see in the Next log.

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STEP B — build the idea live

Delegate all convex/ code to the convex-expert subagent. New tables, queries, mutations, actions, indexes for the user's feature — hand them to convex-expert so they push cleanly the first time (object-form syntax, validators, .withIndex not .filter, internal-vs-public). You own the product decisions, the UI, and the live narration; the subagent owns backend correctness.

Build visible-first, backend-in-parallel — this is the #1 perceived-latency rule.

1. Static UI first. app/page.tsx renders the full feature layout with hardcoded sample data in your first edit. The user sees real pixels in ~30s.
2. Backend right after, via convex-expert. Swap the hardcoded constants for useQuery(...) once the query exists.
3. Interactivity (drag, animation) last, after real IDs exist.

If your todo list starts with "schema" before "static UI", reorder it.

Narrate through the Chef panel, not chat. These five calls are what make the build feel alive. Prefer the Convex MCP run tool (mcp__convex__run) for them — it skips the npx convex run CLI cold-start. (CLI is the fallback if MCP isn't wired.)

• Seed a checklist first, ordered visible-first (4–6 short items). It pulses in the panel: todos:plan → {"items":["Static layout with sample data","Schema + queries","Wire layout to live data","Interactivity","Polish & ship"]} Advance one at a time, immediately after each step finishes — never chain advances (the bar would jump and flicker): todos:advance → {}.
• Post progress every time you touch code (floor, not option — if >~30s pass silently, you're behind): progress:post → {"message":"Wiring schema for the live board","kind":"step"}. Kinds: step (in-flight), shipped (done), note (other). Keep messages ~60 chars.
• Ask ≥1 refinement question before calling v1 done — even on a complete-looking prompt. It renders inline in the panel; do NOT also ask in chat: refinementQuestions:ask → {"text":"Lean playful with bold color, or restrained and elegant?"} Then poll …/refinement-questions.log (or refinementQuestions:listOpen) for the answer — the user's submit flips state to answered. There's no push for file changes, so keep the turn open with a poll loop (~2 min) rather than yielding while a question is open.
• Process feature requests. This plugin's convex-feature-requests monitor watches featureRequests:listPending and pushes a notification when a new request is submitted — even across turns, so prefer that over babysitting the log: you don't have to keep a turn open polling. (The bootstrap's …/feature-requests.log is still there as a fallback if the monitor isn't armed.) When a request arrives, read it (npx convex run featureRequests:listPending or the panel), then for each new {_id,title,description}: featureRequests:setState→inProgress, build it (write the component file first, then wire it into app/page.tsx), featureRequests:setState→completed. Don't batch — do them one at a time in order; the badge transitions are the user's feedback loop. Never touch app/_chef-panel.tsx or the <ChefPanel/> mount while doing this.

HARD RULE: npx convex run is BANNED for verification. Don't poll state with it. To check a feature: read the watcher logs (the function pushed cleanly → the running browser's useQuery has real data), or curl -s http://localhost:PORT | head. If you need fixtures, write ONE internalMutation seedTestData and call it once. The only sanctioned per-step run calls are the panel mutations above (and prefer MCP for those).

Never remove <ChefPanel /> from app/layout.tsx, never delete app/_chef-panel.tsx. It's the user's only channel into the build — mounted in the layout on purpose so redesigning app/page.tsx leaves it intact. Don't unmount it, hide it, or "move it to a tab."

Edit order: write a new component file first, then reference it in app/page.tsx in the same turn — otherwise HMR recompiles between the two writes and the live page flashes a ReferenceError.

After any UI edit, run npx tsc --noEmit — "it compiled" is not enough. The bootstrap's log watchers tail the Convex and Next terminal logs, but a client-only render crash (a dropped component → X is not defined, a string where a branded Id<...> is required) shows only in the browser overlay — never in those logs. next dev's loose HMR typecheck misses this whole class; a clean tsc --noEmit is the gate. This is doubly important right after the convex-expert subagent rewrites a file (it can silently drop an export the page imports — a green push hides it).

Pre-yield checklist — verify ALL before your final message:

1. npx tsc --noEmit is clean (catches dropped components / Id type errors the log watchers never see).
2. todos:listAll shows every item done (a lingering active item reads as "agent quit halfway").
3. Your last progress:post was kind:"shipped" with a v1 summary.
4. app/layout.tsx metadata.title names this app, not the scaffold default.
5. refinementQuestions:listOpen shows zero open rows.
6. You re-read the error logs and tailed feature-requests for ~60s after shipping (users click "request a feature" in the first minute; don't yield before then).

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STEP C — publish (only when the user asks)

The dev scaffold does not pre-wire deployment. When the user says "deploy it" / "publish" / "share with my friend":

npm install @convex-dev/static-hosting
npx @convex-dev/static-hosting setup     # interactive wizard — USE THIS, see note
npx convex login                         # if needed
npx convex deploy -y                     # the -y is required (see note); pushes backend to prod
npx @convex-dev/static-hosting upload --build --prod --dist ./out   # build + upload the static site

The deploy prints the public https://<deployment>.convex.site URL — pass it back to the user. Don't run this proactively — the dev bootstrap skips it on purpose (the install step had a hang rate).

Three sharp edges this avoids (all observed in real runs):

• Use the interactive setup wizard — do NOT hand-copy the static-hosting README's "Manual Setup" snippet. The README is out of date vs the CLI: it exports only the singular functions, but upload calls the plural batch functions (generateUploadUrls, recordAssets) plus getCurrentDeployment, so a manual setup fails with Could not find function for 'staticHosting:generateUploadUrls'. The wizard generates the correct convex/staticHosting.ts.
• npm run deploy / the wizard's deploy shells out to npx convex deploy without -y, which then dies with Cannot prompt for input in non-interactive terminals. Run the two commands above yourself with -y instead of npm run deploy.
• Auth keys don't carry to prod. If the app uses Convex Auth, set JWT_PRIVATE_KEY / JWKS / SITE_URL on the prod deployment before this (set SITE_URL to the public origin, not localhost). Pass a multi-line PEM as "$(cat key.pem)" — env set mangles pasted newlines silently. Then verify with npx convex env list --prod.

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For the complete, current rule set (production gotchas, code-level patterns, log-watcher edge cases), the runbook you fetched in step 2 is canonical. When in doubt, follow it over this summary.