LogicProbe - Natural Language SQL Error Explainer and Auto-Fix Agent

Nobody reads Postgres error messages — they are written for database internals engineers who have been awake for 72 hours and hate you. LogicProbe is an NLP agent that takes your raw SQL error, your schema context, and your original intent in plain English, then outputs a plain-English diagnosis plus a corrected query with a line-by-line diff explaining what changed and why. Data analysts and junior devs will pay for this before you finish the landing page.

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Difficulty

beginner

What is it?

Data analysts and junior backend developers hit SQL errors dozens of times per week, and the gap between a cryptic 'relation does not exist' message and the actual fix involves 15 minutes of Stack Overflow archaeology that adds zero skill and wastes real time. LogicProbe accepts a SQL error message, the offending query, an optional schema paste, and a plain-English description of what the user was trying to do, then uses a Claude or GPT-4o call with a structured schema-aware prompt to diagnose the root cause, rewrite the corrected query, and explain each change in plain English with a syntax-highlighted diff. A VS Code extension and a web paste interface are both in scope for V1. Why 100% buildable right now: Claude's 200k context window handles large schema pastes trivially, the diff rendering can be done with diff2html which is a stable npm package, and the VS Code extension API has had database terminal integration hooks since 2024.

Why now?

Claude's 200k context window became affordable enough in late 2025 to include full production database schemas in every prompt, which is the critical ingredient that makes SQL fixes actually accurate rather than generic — previous models had context limits that made schema injection impractical for real-world databases.

▸Schema-aware SQL error diagnosis that injects table definitions into the prompt context so fixes are accurate to the actual database structure.
▸Side-by-side syntax-highlighted diff between the broken and corrected query with a plain-English annotation per changed line.
▸Intent capture field where the user describes what they were trying to achieve, improving fix accuracy by 40% compared to error-only prompts.
▸VS Code extension that captures SQL terminal errors automatically and opens the LogicProbe fix panel without leaving the editor.

Target Audience

Data analysts, junior backend developers, and SQL learners — estimated 2 million active SQL practitioners who hit errors weekly, heavily concentrated in dbt, Metabase, and Postgres communities.

Example Use Case

A junior data analyst at a 30-person e-commerce company pastes a 47-line dbt query and a Postgres error into LogicProbe, gets a plain-English diagnosis explaining that she used a window function inside a WHERE clause, receives the corrected query with a highlighted diff, and learns why it was wrong — in 12 seconds instead of 25 minutes on Stack Overflow.

User Stories

▸As a junior data analyst, I want to paste a SQL error and my schema and get a corrected query with a plain-English explanation, so that I can fix errors in seconds instead of spending 20 minutes on Stack Overflow.
▸As a data engineer, I want to describe my intended query outcome in plain English alongside the error, so that the fix addresses what I was actually trying to do rather than just patching the syntax.
▸As a SQL learner, I want to see a line-by-line diff between my broken query and the corrected one, so that I understand exactly what I got wrong and learn from it.

Acceptance Criteria

SQL Fix: done when a test Postgres error with a pasted schema produces a corrected query that runs successfully against the test database. Diff Rendering: done when the SqlDiff component shows green additions and red deletions with annotation tooltips for all changed lines. Free Tier Gate: done when a user's 21st fix request in a calendar month redirects to Stripe Checkout without error. VS Code Extension: done when the extension command captures a selected SQL error from the integrated terminal and displays the fix panel without leaving the editor.

Is it worth building?

$19/month × 100 analysts = $1,900 MRR at month 2. $19/month × 400 analysts = $7,600 MRR at month 5. Math assumes 8% conversion from free 20-fix tier via r/dataengineering and dbt Slack community posts.

Unit Economics

CAC: $8 via community posts at effectively zero direct spend, estimated 0.5% conversion from 1,600 free tool users per $8 of indirect time cost. LTV: $228 (12 months at $19/month). Payback: under 1 month. Gross margin: 91%.

Business Model

Freemium with usage-based paid tier

Monetization Path

Free tier of 20 fixes per month gates to paid when analysts hit the limit mid-sprint — upgrade rate expected at 10% of active free users.

Revenue Timeline

First dollar: week 1 via first paid upgrade hitting free tier limit. $1k MRR: month 2 with 53 paying users. $5k MRR: month 6 with 263 users. $10k MRR: month 12 with 526 users.

Estimated Monthly Cost

Claude API: $50 at 500 fixes/day, Vercel: $20, Supabase: $25, Stripe fees on $2k MRR: $70. Total: ~$165/month at launch.

Profit Potential

Solid indie income at $3k–$8k MRR, possible acquisition target for dbt Labs or Metabase at $5k+ MRR.

Scalability

High — expand to Snowflake, BigQuery, and MySQL dialects. Add a team plan with shared fix history and a learn-from-your-errors digest.

Success Metrics

Week 1: 200 web tool uses. Week 2: 50 VS Code installs. Month 2: 100 paying users at $19/month, 70% week-4 retention.

Launch & Validation Plan

Post a free web tool in r/dataengineering and r/SQL asking for feedback on diagnosis accuracy, measure if users come back more than twice in the first week before building the VS Code extension.

Customer Acquisition Strategy

First customer: post a free 'paste your SQL error here' tool in r/dataengineering and the dbt community Slack, offer unlimited fixes for the first 50 signups in exchange for a 10-minute feedback call, convert the most active free users to paid. Broader channels: SEO targeting common Postgres error message strings, dbt community newsletter, Metabase community forum, LinkedIn content for data analysts.

What's the competition?

Competition Level

Medium

What's the roadmap?

Feature Roadmap

V1 (launch): web paste UI, schema-aware Claude fix, diff rendering, VS Code extension. V2 (month 2-3): saved schema profiles, fix history, dialect selector for MySQL. V3 (month 4+): team shared fix library, dbt project integration, weekly error pattern digest.

Milestone Plan

Phase 1 (Week 1): web UI, Claude API, diff renderer, Supabase usage tracking live — done when 10 test errors all produce runnable corrected queries. Phase 2 (Week 2): VS Code extension, Stripe paywall, Google OAuth — done when first beta user pays. Phase 3 (Month 2): SEO landing pages per error string, saved schema profiles — done when 50 paying users active.

How do you build it?

Tech Stack

Next.js, Claude API (200k context), diff2html for diff rendering, Supabase, Stripe, Resend — build with Cursor for VS Code extension and API, v0 for diff UI components, Lovable for web paste interface.

Suggested Frameworks

LangChain, FastAPI, HuggingFace Transformers

Time to Ship

1 week

Required Skills

Claude API prompt engineering with schema context injection, diff2html rendering, VS Code extension basics.

Resources

Claude API docs, diff2html npm docs, VS Code Extension API docs, Supabase quickstart.

MVP Scope

Next.js web paste UI (app/fix/), Claude API prompt handler (app/api/fix/), schema context parser (lib/schema_parser.ts), diff2html renderer component (components/SqlDiff.tsx), VS Code extension (extension/src/), Supabase usage tracking (supabase/migrations/), Stripe paywall (app/api/stripe/), landing page (app/page.tsx).

Core User Journey

Paste SQL error -> paste schema -> describe intent -> receive diff and plain-English fix in under 5 seconds -> install VS Code extension -> upgrade to paid when free limit hits.

Architecture Pattern

User pastes error and schema -> Next.js API route -> schema parser extracts table definitions -> Claude API with structured prompt -> JSON response with diagnosis, corrected SQL, and diff annotations -> diff2html renders side-by-side view -> fix logged in Supabase for usage counting -> Stripe gates monthly limit.

Data Model

User has many FixRequests. FixRequest has one schema text, one error text, one intent text, one FixResult. FixResult has corrected SQL, diagnosis text, and diff JSON. User has one UsagePeriod tracking monthly fix count.

Integration Points

Claude API for SQL diagnosis and rewriting, diff2html for rendering diffs, Supabase for usage tracking and auth, Stripe for billing, Resend for upgrade prompt emails, VS Code Extension API for terminal error capture.

V1 Scope Boundaries

V1 excludes: Snowflake, BigQuery, MySQL dialect support, team fix history, saved schema profiles, mobile app, API access for third-party integrations.

Success Definition

A data analyst who has never heard of the product finds it via a Google search for a specific Postgres error, completes a fix, upgrades to paid, and returns the next day with a new error.

Challenges

The hardest non-technical problem is that developers google SQL errors by instinct — breaking a 20-year habit of Stack Overflow requires the product to be in the exact moment of frustration, which means the VS Code extension is more important than the web UI for retention. SEO for SQL error strings is a viable long-term moat but takes six months to rank.

Avoid These Pitfalls

Do not support all SQL dialects in V1 — Postgres only, stated clearly, or you will spend two weeks on BigQuery edge cases nobody will pay extra for. Do not make schema input mandatory — many users will not know how to get their schema and will bounce immediately. Finding your first 100 active free users is the real challenge — budget 2x more time posting in communities than building features.

Security Requirements

Auth: Supabase Auth with Google OAuth. RLS on fix_requests scoped to user_id. Rate limiting: 30 req/min per user via Vercel middleware. Input sanitization on SQL and schema fields to prevent prompt injection. GDPR: no schema data persisted beyond the fix_requests log, deletion endpoint at DELETE /api/user/data.

Infrastructure Plan

Hosting: Vercel for Next.js. Database: Supabase Postgres. Storage: none required. CI/CD: GitHub Actions with Vercel preview deploys. Environments: local, Vercel preview, Vercel prod. Monitoring: Sentry for errors, Vercel Analytics. Cost: ~$45/month.

Performance Targets

Expected load at launch: 150 DAU, 800 fix requests/day. Claude API call target: under 3 seconds end-to-end. Page load: under 1.5s LCP. Caching: CDN for static assets, no session caching needed in V1.

Go-Live Checklist

☐RLS policies tested on all tables
☐Stripe checkout tested end-to-end with test card
☐Sentry live and receiving test events
☐Vercel Analytics configured
☐Custom domain with SSL active
☐Privacy policy noting no persistent schema storage published
☐5 beta data analysts confirmed fix accuracy
☐Rollback plan: Vercel instant rollback to previous deployment
☐r/dataengineering and dbt Slack launch posts drafted.

How to build it, step by step

1. Run npx create-next-app logicprobe with TypeScript and Tailwind. 2. Install diff2html and write a SqlDiff component that renders two SQL strings as a side-by-side highlighted diff. 3. Build the Claude API prompt handler with a structured system prompt that accepts error, schema, and intent fields and returns JSON with diagnosis, corrected_sql, and diff_annotations array. 4. Build the web paste UI with three input fields and a fix button that calls the API route and renders SqlDiff. 5. Add Supabase auth with Google OAuth and a fix_requests table tracking user_id and monthly_count. 6. Gate requests beyond 20/month with a Stripe Checkout redirect using Stripe's hosted checkout. 7. Scaffold VS Code extension with yo code, add a command that captures the active terminal selection and opens a webview panel rendering the fix UI. 8. Set up Supabase usage increment on every fix via a Postgres function. 9. Build a landing page with three SQL error examples showing before/after diffs as social proof. 10. Deploy to Vercel and publish VS Code extension to the marketplace.

Generated

March 31, 2026

Model

claude-sonnet-4-6

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