Memstack Security Rls Checker logo

Memstack Security Rls Checker

Community
cwinvestments
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.

Overview

Publishercwinvestments
Repositorymemstack
Skill namememstack-security-rls-checker
Stars
425
Forks
45
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cwinvestments on GitHub. Read the source before you install it.

Installation

Install the Memstack Security Rls Checker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cwinvestments/memstack/tree/master/skills/security/rls-checker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cwinvestments/memstack.git /tmp/memstack
mkdir -p .claude/skills
cp -r /tmp/memstack/skills/security/rls-checker .claude/skills/memstack-security-rls-checker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memstack Security Rls Checker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Memstack Security Rls Checker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Memstack Security Rls Checker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

🔒 RLS Checker: Auditing Row Level Security...

Audit Supabase Row Level Security policies across all tables in a project.

Activation

When this skill activates, output:

🔒 RLS Checker: Auditing Row Level Security...

Then execute the protocol below.

Context Guard

ContextStatus
User asks to check/audit RLSACTIVE: full audit
User mentions Supabase securityACTIVE: full audit
User asks about table permissionsACTIVE: full audit
User is writing RLS policiesDORMANT, they know what they're doing
Non-Supabase projectDORMANT: not applicable

Protocol

Step 1: Discover Tables

Find all Supabase tables referenced in the project. Search in priority order:

  1. Migration files: most authoritative source:

    bash
    find . -path "*/migrations/*.sql" -o -path "*/supabase/migrations/*.sql" | head -50

    Look for CREATE TABLE statements.

  2. Generated types: comprehensive if available:

    types/database.ts, types/supabase.ts, src/types/database.types.ts, database.types.ts

    Parse the Tables interface for all table names.

  3. Client usage: catches tables missed by above:

    bash
    grep -r "\.from(['\"]" --include="*.ts" --include="*.tsx" --include="*.js"

    Extract table names from .from('table_name') calls.

  4. Storage buckets: separate RLS surface:

    bash
    grep -r "storage\.from\|createBucket\|storage-api" --include="*.ts" --include="*.tsx" --include="*.sql"

Compile a deduplicated list of all tables and storage buckets.

Step 2: Extract RLS Policies

For each table, find its RLS configuration:

  1. Search migration SQL for RLS statements:

    • ALTER TABLE <name> ENABLE ROW LEVEL SECURITY: RLS is on
    • CREATE POLICY statements: extract policy name, operation (SELECT/INSERT/UPDATE/DELETE/ALL), and USING/WITH CHECK expressions
    • ALTER TABLE <name> FORCE ROW LEVEL SECURITY: RLS enforced even for table owners
  2. Check for intentionally unprotected tables:

    • Tables with GRANT SELECT ON <table> TO anon without RLS are intentionally public
    • Look for comments like -- public table, -- no RLS needed, or -- rls:skip in migration SQL
    • Tables marked with -- rls:skip should be classified as ✅ OK (Intentional) in the report, not flagged as missing RLS. This lets teams explicitly document tables that rely on application-level authorization (e.g., service-role-first architectures).
    • If no -- rls:skip marker exists and no RLS is enabled, flag normally.
  3. Check Supabase dashboard-configured policies:

    • Policies created via the Supabase Dashboard do NOT appear in migration SQL files. They exist only in the live database.
    • If a table has no RLS in migration files but is referenced in application code, note in the report: "⚠️ Policy may exist in Supabase Dashboard, verify via supabase inspect db policies or the Dashboard UI."
    • Search for Supabase CLI config files (supabase/config.toml, .supabase/) that might indicate whether the project uses Dashboard-managed policies.
    • Recommend teams capture all Dashboard-created policies in migration files for auditability:
      bash
      supabase db dump --schema public --data-only=false | grep -A5 "CREATE POLICY"
  4. Check Supabase dashboard seed/init files for policy definitions that may not be in migrations.

Step 3: Analyze Policies

For each table with RLS enabled, evaluate policy quality:

Check 1, Operation Coverage: Flag tables missing policies for any CRUD operation:

  • Has SELECT but no INSERT → partial coverage (WARNING)
  • Has SELECT but no UPDATE/DELETE → partial coverage (WARNING)
  • Has no policies at all despite RLS enabled → locked out (CRITICAL)

Check 2, User Isolation: Verify policies filter by authenticated user:

  • auth.uid() in USING clause: standard user isolation (OK)
  • auth.uid() in WITH CHECK clause: write isolation (OK)
  • No auth.uid() reference: overly permissive (WARNING)
  • Hardcoded UUIDs instead of auth.uid(): security risk (CRITICAL)
  • current_setting('app.*') instead of auth.uid(): anti-pattern (WARNING). This relies on the application explicitly setting a PostgreSQL session variable before every query. If the variable is unset, the policy may fail open or closed unpredictably. Prefer auth.uid() which Supabase populates automatically from the JWT. Flag with:
    bash
    grep -rn "current_setting" --include="*.sql"

Check 3, Multi-Tenant Isolation: For tables with organization_id or team_id columns:

  • Policy should JOIN against a membership table to verify org access
  • Direct organization_id = <value> without membership check is insufficient (WARNING)
  • Example of correct pattern:
    sql
    CREATE POLICY "org_isolation" ON documents
      USING (organization_id IN (
        SELECT org_id FROM organization_members
        WHERE user_id = auth.uid()
      ));

Check 4, Service Role Bypass: Search codebase for service role usage that bypasses RLS:

bash
grep -r "service_role\|serviceRole\|supabaseAdmin\|SUPABASE_SERVICE_ROLE" --include="*.ts" --include="*.tsx" --include="*.js" --include="*.env*"
  • Server-side API routes using service role: acceptable if intentional (INFO)
  • Client-side code with service role key: critical vulnerability (CRITICAL)
  • Service role in .env committed to git: critical vulnerability (CRITICAL)

Check 5, Storage Bucket Policies: For each storage bucket found:

  • Check for bucket-level RLS policies in migrations
  • Verify upload policies restrict by user path (e.g., auth.uid()::text = (storage.foldername(name))[1])
  • Flag buckets with no policies (WARNING)

Step 3b: Defense-in-Depth Score (Service-Role Architectures)

If the project uses service role for most/all database access (Check 4 found widespread supabaseAdmin / SUPABASE_SERVICE_ROLE usage), compute a defense-in-depth score:

Score calculation:

  • Count total tables with sensitive data (contains passwords, tokens, PII, financial data, or credentials)
  • Count how many of those tables have RLS enabled
  • Score = (tables with RLS / total sensitive tables) × 100

Classification:

ScoreRatingMeaning
80 to 100%🟢 StrongRLS provides meaningful backup even though service role bypasses it
50 to 79%🟡 PartialSome defense-in-depth but gaps remain
20 to 49%🟠 WeakMost sensitive tables unprotected at DB layer
0 to 19%🔴 NoneEntire security model depends on application code: single bug = full breach

Include in report:

## Defense-in-Depth Score
Architecture: Service-role-first (all API routes use service role key)
Sensitive tables: <count>
Sensitive tables with RLS: <count>
Score: <percentage>: <rating>

Note: Service role bypasses RLS by design. This score measures how well
the database would protect data if an application-level auth bug occurred.

Recommendation for low scores: Even in service-role architectures, enabling RLS on sensitive tables provides a safety net. If a developer accidentally uses the anon key, creates a new route without auth, or a future refactor introduces a bug, RLS prevents cross-tenant data access at the database layer.

Step 4: Generate Report

Output a structured report with this format:

🔒 RLS Audit Report
Project: <project-name>
Tables found: <count>
Storage buckets: <count>

## Table Audit

| Table | RLS | Policies | Coverage | Risk | Issue |
|-------|-----|----------|----------|------|-------|
| users | ON | 4 | Full | ✅ OK | none |
| documents | ON | 2 | Partial | ⚠️ WARN | Missing DELETE policy |
| payments | OFF | 0 | None | 🔴 CRIT | No RLS enabled |
| public_posts | OFF | 0 | N/A | ✅ OK | Intentionally public (-- rls:skip) |

## Storage Buckets

| Bucket | Policies | Risk | Issue |
|--------|----------|------|-------|
| avatars | 2 | ✅ OK | none |
| uploads | 0 | ⚠️ WARN | No upload restriction |

## Critical Issues
1. **payments**: No RLS enabled. Any authenticated user can read/write all rows.
   → Fix: `ALTER TABLE payments ENABLE ROW LEVEL SECURITY;` then add user-scoped policies.

2. **service_role in client**: Found in `src/lib/supabase.ts:14`.
   → Fix: Remove service role key from client code. Use server-side API route instead.

## Warnings
1. **documents**: Missing DELETE policy. Users may not be able to delete their own documents, or deletion may be unrestricted.
   → Fix: Add `CREATE POLICY "delete_own" ON documents FOR DELETE USING (user_id = auth.uid());`

2. **uploads bucket**: No storage policies defined.
   → Fix: Add bucket policies restricting uploads to user-specific paths.

## Summary
- 🔴 Critical: <count>
- ⚠️ Warning: <count>
- ✅ OK: <count>
- Total tables: <count>

Step 5: Suggest Fixes

For each CRITICAL and WARNING issue, provide:

  1. The exact SQL migration to fix it
  2. Where to add it (new migration file name following project conventions)
  3. Any application code changes needed (e.g., removing service role from client)

Offer to generate a migration file with all fixes: supabase/migrations/<timestamp>_rls_fixes.sql

Risk Levels

LevelMeaningAction
🔴 CRITICALData exposed or writable by unauthorized usersFix immediately
⚠️ WARNINGIncomplete coverage or weak isolationFix before production
ℹ️ INFOAcceptable pattern that should be verifiedReview and confirm intentional
✅ OKProperly securedNo action needed

Common Patterns Reference

User-owned rows:

sql
CREATE POLICY "users_own_data" ON table_name
  FOR ALL USING (user_id = auth.uid())
  WITH CHECK (user_id = auth.uid());

Org-scoped with membership check:

sql
CREATE POLICY "org_members_access" ON table_name
  FOR ALL USING (
    organization_id IN (
      SELECT org_id FROM organization_members
      WHERE user_id = auth.uid()
    )
  );

Public read, authenticated write:

sql
CREATE POLICY "public_read" ON table_name FOR SELECT USING (true);
CREATE POLICY "auth_insert" ON table_name FOR INSERT WITH CHECK (auth.role() = 'authenticated');

Storage bucket user isolation:

sql
CREATE POLICY "user_uploads" ON storage.objects
  FOR INSERT WITH CHECK (
    bucket_id = 'uploads' AND
    auth.uid()::text = (storage.foldername(name))[1]
  );

Level History

  • Lv.1: Base: Table discovery (migrations, types, client usage), RLS policy extraction, 5-point analysis (coverage, user isolation, multi-tenant, service role, storage), structured report with risk levels, fix generation. Patterns derived from AdminStack, EpsteinScan, and 10+ production Supabase projects. (Origin: MemStack Pro v1.0, Mar 2026)
  • Lv.2: Audit feedback: Added current_setting() anti-pattern detection (vs auth.uid()), -- rls:skip marker for intentionally unprotected tables, Supabase Dashboard policy detection guidance (supabase inspect db policies), defense-in-depth score for service-role-first architectures. (Origin: AdminStack audit, Mar 2026)

Frequently asked questions

What does the Memstack Security Rls Checker AI skill do?

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.

Why use Memstack Security Rls Checker on TypingMind?

Because you install it once and use it with any model. Memstack Security Rls Checker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Memstack Security Rls Checker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cwinvestments/memstack/tree/master/skills/security/rls-checker. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memstack Security Rls Checker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Memstack Security Rls Checker?

As many as you like. As long as a model supports skills, you can use Memstack Security Rls Checker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Memstack Security Rls Checker AI skill free?

Yes. It is published on GitHub by cwinvestments under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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