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 preset-io/agent-skills --skill preset-embedded-rlsgit clone --depth 1 https://github.com/preset-io/agent-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/preset-io/agent-skills/preset-embedded-rls)<a href="https://agentmods.dev/skills/preset-io/agent-skills/preset-embedded-rls"><img src="https://agentmods.dev/badge/skills/preset-io/agent-skills/preset-embedded-rls/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/skills/preset-io/agent-skills/preset-embedded-rls"><img src="https://agentmods.dev/badge/skills/preset-io/agent-skills/preset-embedded-rls.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 | $0.00049 | $0.00303 |
| Opus 5 | $0.00024 | $0.00151 |
| Sonnet 5 | $0.00010 | $0.00061 |
| Haiku 4.5 | $0.00005 | $0.00030 |
Grade A, and why
preset-embedded-rls 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 11d 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
preset-embedded-rls
Use before guest-token creation when embedded viewers need row-level security.
Always
- Auth and conventions come from
preset-api(JWT exchange, base URLs, Rison); resolve the workspace hostname through the Management API when it is not already known. Consult metadata skills only when column validation is required. - Do not invent tenant identifiers, filters, dataset columns, or access rules.
- Treat RLS clauses as permission controls that can leak or hide customer data.
- Confirm every clause and intended viewer population before token creation.
- Do not validate with broad data-returning queries unless the user approves target and limit.
Decision Rules
- Classify embedded RLS review as plan-only safety work.
- Identify tenant and user filter safety issues.
- Require approval before clauses are used in token claims.
- Avoid mutating embedded RLS configuration.
Workflow Order
- Inspect embedded RLS clauses.
- Flag unsafe tenant and user filters.
- Summarize approval requirements.
- Stop before using clauses in tokens.
Retrieve
- Embedded RLS rule design and review: references/embedded-rls-rules.md
What ships with it
1 file 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.
- 11d ago First seen · 35 lines · 49 tokens per session scan A 6c79f7a2a44e
preset-embedded-rls is a skill published in the GitHub repository preset-io/agent-skills (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 49 tokens to every session and 303 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.
Other skills, from other repositories
business-intelligence-expert
Build comprehensive business intelligence solutions including data warehouses, ETL pipelines, interactive dashboards, and analytical reporting systems. Use when the user mentions BI, data warehousing, star or snowflake schemas, OLAP cubes, ETL/ELT pipelines, dashboards, KPIs, or analytical reporting.
edinet-company-ir-tracker
A workflow for retrieving recent filings from Japan’s EDINET system, the government database for corporate disclosures, for one Japanese company. It groups filings into areas such as financial reports, takeovers, ownership, governance, and events.
search-procurement-portal
A skill for searching Japanese government procurement notices from the official 官公需情報ポータル. It discovers tender announcements with details such as project name, issuing organization, category, and procedure type.
edinet-document-search
A search tool for Japanese financial disclosure documents filed through EDINET, Japan’s government system for company filings. It covers reports such as annual, quarterly, ownership, takeover, and internal-control disclosures.
search-egov-laws
A skill for searching Japanese laws, cabinet orders, and ministerial ordinances through Japan's official e-Gov legal information API. It returns details such as a law's title, identifier, type, and enforcement date.
edinet-document-fetch
A tool for downloading a specific Japanese financial disclosure from EDINET, Japan’s official system for company filings, after its document ID has been found.