lovable-prompting-playbook
Skill10 starsChanged 6 months agoMITView on GitHub
Use when planning a session in Lovable, breaking a feature into prompts, asking Lovable to refactor or debug, or writing a backlog of Lovable prompts. Not when actually executing inside Lovable — this guides how to phrase work, not the work itself.
In this repository56 other files it gives its agents.
- accessibility-passSkill
- analytics-eventsSkill
- api-error-handlingSkill
- audit-logging-backendSkill
- background-jobs-and-cronSkill
- dark-mode-and-themingSkill
- dashboard-charts-rechartsSkill
- edge-functions-and-webhooksSkill
- email-and-notificationsSkill
- error-states-and-empty-uiSkill
- feature-flags-and-togglesSkill
- file-upload-storageSkill
- i18n-multi-languageSkill
- lovable-ship-checklistSkill
- migration-playbookSkill
- multi-tenant-backendSkill
- onboarding-copySkill
- optimistic-updatesSkill
- payment-failed-and-recoverySkill
- payment-webhook-idempotencySkill
- payment-webhook-testingSkill
- performance-budgetSkill
- pg-cron-recipesSkill
- pg-cron-scheduled-jobsSkill
- pg-cron-with-pg-net-edgeSkill
- postgres-full-text-searchSkill
- postgres-triggers-and-functionsSkill
- pricing-and-billingSkill
- programmatic-seo-content-qualitySkill
- programmatic-seo-pagesSkill
- rate-limiting-edgeSkill
- realtime-and-subscriptionsSkill
- refactor-safe-diffSkill
- release-notesSkill
- seo-core-web-vitalsSkill
- seo-landing-pageSkill
- seo-meta-tags-spaSkill
- seo-sitemap-generationSkill
- seo-ssr-and-prerenderingSkill
- seo-structured-dataSkill
- server-input-validationSkill
- shadcn-patternsSkill
- soft-delete-and-archivingSkill
- stripe-one-time-payment-webhooksSkill
- stripe-payment-webhooksSkill
- supabase-auth-flowsSkill
- supabase-database-designSkill
- supabase-rls-and-authSkill
- support-replySkill
- tanstack-infinite-queriesSkill
- tanstack-mutations-and-invalidationSkill
- tanstack-prefetch-and-hydrationSkill
- tanstack-query-alternativeSkill
- tanstack-router-data-loadersSkill
- tanstack-start-server-routesSkill
- typed-api-hooks-formsSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 39
After each prompt, review the chat diff before moving on.
- Very specific: line 46
Use a skill invocation (/refactor-safe-diff) to constrain scope.
- Very specific: line 73
Add knowledge entries for: stack, brand, banned patterns (any, inline Supabase in components).
- Distinctive: line 74
Add this repo's skills to your workspace and let descriptions trigger them automatically.
- Very specific: line 75
Keep prompts in a note (Linear, Notion, or prompts.md) so they can be re-run on a new branch.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Lovable prompting playbook
Lovable does best with small, specific, verifiable prompts. This skill helps you (or another agent) write prompts that ship.
Anatomy of a good Lovable prompt
[Context] What part of the app, which files or routes.
[Goal] What "done" looks like in user-visible terms.
[Approach] Optional. Pick stack / pattern if it matters.
[Limits] What NOT to change.
[Verify] How you'll check it works.Example:
On the
/dashboardroute, add a "Revenue (30d)" KPI card to the top row, usinguseRevenue30d(already exists). Show value, % delta, and a sparkline. Do not touch the other cards. I'll verify by loading the dashboard and comparing the number to Stripe.
Sequence work into prompts
Break a feature into 3–7 prompts, each shippable on its own:
- Schema — tables, RLS, types (
migration-playbook). - API — fetchers + hooks (
typed-api-hooks-forms). - UI shell — empty/loading/error layout (
error-states-and-empty-ui). - Wiring — connect UI to hooks.
- Edge cases — auth, permissions, validation.
- Polish — copy, a11y, mobile.
After each prompt, review the chat diff before moving on.
When Lovable goes off course
- "Revert to the previous version and try again, keeping
Xunchanged." - "Show me only the diff for
path/to/file.tsx." - "Stop. Summarize what you changed in the last 3 messages before continuing."
- Use a skill invocation (
/refactor-safe-diff) to constrain scope.
Pair prompts with skills
| Task | Skills to invoke |
| New feature with Supabase | /supabase-database-design then /typed-api-hooks-forms |
| New form | /typed-api-hooks-forms (covers Zod + RHF) |
| Stripe webhook | /stripe-payment-webhooks + /payment-webhook-idempotency |
| Before launch | /lovable-ship-checklist |
| Slow page | /performance-budget |
Phrases that work well
- "Implement X. Do not change Y."
- "Add a checklist comment in the PR description listing every file you touched."
- "If you are unsure between two approaches, ask me before writing code."
- "Reuse the existing
useFoohook; do not create a duplicate."
Phrases that cause trouble
- "Improve the app." — undefined target.
- "Refactor everything." — invites scope creep; use
/refactor-safe-diffwith explicit files. - "Add a few small fixes." — bundle multiple things; you'll lose track of regressions.
Workspace setup recommendations
- Add knowledge entries for: stack, brand, banned patterns (
any, inline Supabase in components). - Add this repo's skills to your workspace and let descriptions trigger them automatically.
- Keep prompts in a note (Linear, Notion, or
prompts.md) so they can be re-run on a new branch.
Avoid
- Mega-prompts that mix schema, UI, billing, and copy.
- Trusting Lovable to "remember" earlier sessions — assume zero memory across chats.
- Letting failing diffs accumulate; revert early.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

