Schemap vs. LLMSchema & DbSketch

Direct Answer: LLMSchema and DbSketch are basic schema dumper scripts that output raw Markdown tables. Schemap is a deterministic Database Intelligence Layer that computes AI readiness scores, injects negative safety rules into agent configs, filters role-based context, and solves multi-table JOIN paths in sub-3ms.

Capability / Feature	`LLMSchema` / `DbSketch`	Schemap v3.1.0
AI Anti-Hallucination Rules	❌ None (AI guesses JOIN paths)	✅ Automatic `[SAFETY]` rule generator (`schemap agents`)
Role-Scoped Context Filtering	❌ No (Dumps entire DB)	✅ `--scope analytics`, `backend`, `core`
Candidate Foreign Key Inference	❌ No (Fails on undeclared FKs)	✅ Confidence scoring & interactive fix (`schemap fix`)
Token & Monetary Cost Benchmark	❌ No cost reporting	✅ Token compression & USD savings tracker (`schemap benchmark --cost`)
Shortest Join Path Solver	❌ No	✅ Built-in BFS Join Graph Solver (`schemap join`)
AI Readiness Score	❌ No	✅ Diagnostic health suite 0-100 (`schemap doctor`)
Git Pre-Commit Automation	❌ Manual run only	✅ One-click Git pre-commit hook (`schemap hook install`)
Compilation Latency	~100ms - 200ms	Sub-3ms deterministic compilation

When to Pick LLMSchema or DbSketch

• You have a tiny SQLite or Postgres database with fewer than 5 tables.
• You only need a quick one-off raw Markdown printout for a prompt.
• You do not use persistent AI agent rule files (`CLAUDE.md`, `AGENTS.md`, Cursor `.mdc`).

When to Pick Schemap

• You work with production schemas (10 to 500+ tables) with implicit foreign key constraints.
• You want to stop AI agents from guessing invalid JOINs (e.g. `orders.id = users.id`) or querying sensitive password hashes.
• You want to save up to 80% on LLM input token bills with role-scoped profiles (`--scope analytics`).
• You want automated context synchronization across team developers with Git pre-commit hooks (`schemap hook install`).

1. Anti-Hallucination Safety Guardrails

Raw schema dumpers simply list tables. When an AI agent encounters columns like user_id and account_id, it often guesses the wrong JOIN sequence. Schemap generates explicit negative guardrails in CLAUDE.md and AGENTS.md:

## AI Safety & Anti-Hallucination Guardrails
- [SAFETY] Never join `orders.id` directly to `users.id`. Correct JOIN path: `orders.user_id -> users.id`.
- [SAFETY] Sensitive Data Protection: Never query or expose raw credentials: `users.password_hash`
- [SAFETY] Immutability Guardrail: Do not generate DELETE or UPDATE queries for audit/financial records: `payments`

2. Role-Scoped Context Profiles

Dumping 100 tables into an LLM context window wastes 50,000+ tokens and degrades AI reasoning. Schemap allows role-scoped filtering:

# For Data Engineers & Analysts
schemap context --scope analytics

# For Backend Transaction Engineers
schemap context --scope backend

3. Monetary Token Cost Inspector

Schemap includes a built-in cost calculator based on real LLM API rates ($3.00/1M input tokens):

$ schemap benchmark --cost

==================================================
 Database Context Benchmark
==================================================
  Raw Schema:           18,400 tokens
  Schemap Context:       3,200 tokens
  Compression:          82.6%
--------------------------------------------------
  Tokens Saved/Prompt:  15,200
  Cost Saved/Prompt:    $0.0456
  Est. Monthly/Dev:     $91.20
==================================================