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Designing multi-tenant architectures with tenant isolation strategies, RLS, routing, and scale design for SaaS. Use when designing multi-tenant SaaS systems or tenant isolation.

simota/agent-skills Agent Skills author: simota 9 files body ≈ 4 285 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Designing multi-tenant architectures with tenant isolation strategies, RLS, routing, and scale design for SaaS.

As a process C 60/100 · Has gaps — weak spots: when it triggers, inputs and preconditions

ProcedureSupabaseInfrastructureData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
98
Quality 40%
91
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security reference/patterns.md:108
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      **Security invoker views (PostgreSQL 15+):** Use `security_invoker = true` on views over RLS-protected tables so the RLS policies of the caller (not the view owner) are applied, preventing privilege e
    • low Risky intent intent-offensive-security SKILL.md:61
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Design RLS policies that fail closed (deny by default, explicit allow). Always index columns used in RLS policies to avoid sequential scans — missing index causes 100x+ slowdown ([Supabase RLS Perfo

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4285 tokens
    • 100Steps. 79 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 15 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 177: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 79 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.