Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/martinolivero/saas-builder/data-modelingnpx skills add MartinOlivero/saas-builder --skill data-modelinggit clone --depth 1 https://github.com/MartinOlivero/saas-builderWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/martinolivero/saas-builder/data-modeling)<a href="https://agentmods.dev/skills/martinolivero/saas-builder/data-modeling"><img src="https://agentmods.dev/badge/skills/martinolivero/saas-builder/data-modeling.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00120 | $0.01308 |
| Opus 5 | $0.00060 | $0.00654 |
| Sonnet 5 | $0.00024 | $0.00262 |
| Haiku 4.5 | $0.00012 | $0.00131 |
Grade A, and why
data-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Modeling
This skill designs the schema your product runs on, and gets multi-tenant isolation right — the mistake that, when wrong, leaks one customer's data to another.
Analogy: the schema is the foundation and load-bearing walls of a house. You can repaint and rearrange furniture later cheaply; moving a load-bearing wall after move-in is expensive and dangerous. Get the structure right before building on it.
Discovery (max 3 questions, only if unknown)
- Is this multi-tenant (multiple orgs/customers sharing the app) or single-tenant?
- Is the data mostly relational (users, orders, invoices) or document-like/flexible?
- Are you on Supabase, Neon, or another Postgres host — or undecided?
Step 1 — Choose the database
Postgres by default — relational integrity, JSONB for flexible fields, full-text search, and it scales far. Don't reach for NoSQL without a proven document/scale reason. Every option below runs Postgres underneath, so the methodology in this skill (tenant_id + RLS, indexing, migrations) is identical regardless of host. Pick the host by how you're building:
- InsForge — agentic-native. Built so an AI coding agent provisions the DB, runs migrations, sets RLS, and debugs through MCP, without you touching a dashboard. The natural fit when you build with an agent — which is exactly how this plugin runs. Batteries-included (Postgres + auth + RLS + storage + functions), open source. (Its published benchmarks claim faster/cheaper agent task completion vs Supabase — note those are first-party numbers, and it's a younger project (~5k⭐) than Supabase.)
- Supabase — mature ecosystem. Batteries-included (Auth + RLS + Realtime + storage), RLS battle-tested at scale, huge community and docs (~104k⭐). The safe default when ecosystem maturity matters more than agent-native tooling.
- Neon — serverless Postgres with branching/autoscaling; great for preview-per-PR databases.
- Redis only for cache/sessions/real-time; Elasticsearch only for heavy full-text search.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 64 lines · 120 tokens per session scan A e31bf5caf5f7
data-modeling is a skill published in the GitHub repository MartinOlivero/saas-builder (2 stars, last pushed 18d ago), licensed MIT. It adds 120 tokens to every session and 1,308 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
multi-tenancy
Multi-tenant architecture patterns: row-level, schema-level, database-level isolation and tenant routing.
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memstack-development-migration-planner
Use this skill when the user says 'migration', 'schema change', 'database migration', 'alter table', 'add column', 'change type', 'rollback plan', or needs safe database schema evolution with zero-downtime strategies. Do NOT use for initial database design (use database-architect) or code refactoring.
memstack-security-rls-guardian
Use this skill when creating or altering database tables in Supabase or PostgreSQL projects. Triggers include: CREATE TABLE, ALTER TABLE, migration files, 'RLS', 'row level security', 'new table', 'database schema'. Enforces Row Level Security policies on every table to prevent unauthorized data access. Do NOT use for…
memstack-security-rls-checker
Use this skill when the user says 'check RLS', 'audit RLS', 'RLS policies', 'row level security', 'Supabase security audit', or needs to verify table-level access control. Audits Supabase Row Level Security policies across all tables. Do NOT use for non-Supabase projects or writing RLS policies from scratch.
chat-logger
Log all chat messages to a SQLite database for searchable history and audit. Use when: (1) Building chat history, (2) Auditing conversations, (3) Searching past messages, or (4) User asks to log chats.