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 skills add codebygarv/Ai-skills --skill multi-tenant-data-partitionergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWrote 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/codebygarv/ai-skills/multi-tenant-data-partitioner)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/multi-tenant-data-partitioner"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/multi-tenant-data-partitioner.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 | $0.00030 | $0.00414 |
| Opus 5 | $0.00015 | $0.00207 |
| Sonnet 5 | $0.00006 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
multi-tenant-data-partitioner 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 8d 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.
What it actually says
Purpose
Architect secure, cost-effective, and scalable database multi-tenancy models (Shared Database/Shared Schema with Row-Level Security, Database-per-Tenant, Schema-per-Tenant) tailored to compliance, isolation, and cost constraints.
When to Use
- Designing the data architecture for a B2B SaaS application.
- Enterprise customers requesting strict physical data isolation or custom backup schedules.
- Evaluating database costs vs cross-tenant data leakage risks.
What to Analyze
- Isolation Model Selection:
- Shared Database, Shared Schema (Pool): Highest resource efficiency, lowest cost, enforced via Postgres RLS.
- Separate Schema (Bridge): Logical isolation, schema migration overhead at 1,000+ tenants.
- Separate Database (Silo): Complete physical isolation, highest cost, individual tenant disaster recovery.
- Tenant Context Resolution: Subdomain (
tenant.app.com), JWT claim, or custom HTTP header routing. - Connection Pool Management: Dynamic connection routing without exhausting database connection limits.
- Cross-Tenant Leakage Prevention: Automated query filters and database-enforced Row-Level Security (RLS).
- Noisy Neighbor Defense: Per-tenant query rate limits and compute quotas.
Output Format
- Multi-Tenancy Tradeoff Matrix: Comparison of Pool vs Bridge vs Silo for your specific scale.
- Postgres Row-Level Security (RLS) Policy: SQL script enforcing
tenant_idsession boundaries. - Tenant Routing Middleware: Code extracting and injecting tenant context into database queries.
Avoid
- Relying solely on application-level
WHERE tenant_id = :idqueries without database-level RLS enforcement. - Creating 10,000 PostgreSQL schemas on a single instance (causes heavy catalog metadata lock contention).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 37 lines · 30 tokens per session scan A 26e3c74dab12
multi-tenant-data-partitioner is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 30 tokens to every session and 414 once invoked, about $0.0002 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 2026-08-30.
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