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.
git clone --depth 1 https://github.com/kensaurus/cursor-kenjiWrote 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/commands/kensaurus/cursor-kenji/rls-plan)<a href="https://agentmods.dev/commands/kensaurus/cursor-kenji/rls-plan"><img src="https://agentmods.dev/badge/commands/kensaurus/cursor-kenji/rls-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/kensaurus/cursor-kenji/rls-plan"><img src="https://agentmods.dev/badge/commands/kensaurus/cursor-kenji/rls-plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.00169 |
| Opus 5.5 | $0.00006 | $0.00068 |
| Sonnet 5.5 | $0.00003 | $0.00034 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
rls-plan 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 25d 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
RLS Audit Plan
Run the plan-rls-audit skill: audit Supabase/Postgres row-level security
and access control, then produce a remediation plan. Audit and plan only —
write no SQL and change no policies until the plan is approved.
Emit findings ranked by severity (tables without RLS, over-permissive policies,
service_role exposure) with a phased fix checklist. After approval, execute
with db-migrator and backend-patterns.
The full playbook lives in the plan-rls-audit skill.
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.
- 25d ago Changed · +2 lines 27928025c17d
- 1mo ago First seen · 17 lines · 16 tokens per session scan A 11c03f9ceda4
rls-plan is a command published in the GitHub repository kensaurus/cursor-kenji (9 stars, last pushed 20d ago), licensed MIT. It adds 16 tokens to every session and 169 once invoked, about $0.0001 per session on Opus 5.5. 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 commands, from other repositories
db-context-postgres
Validate that a non-Supabase Postgres database (Cloud SQL, RDS, self-hosted) is reachable, introspect a scoped subset of its schema, and persist it as DBCONTEXT.md. Read-only, opt-in. Use when work touches that database and planning needs verified schema, or DBCONTEXT.md is stale. Do not use without a task (run…
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Confidence-based code review checklist.
commit
Git commit with conventional commits format.
mcp
Switch MCP mode to optimize context window.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.