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sg-gov-gen-ai-security

sg-gov-gen-ai-security is a skill for Claude Code from kyleoliveiro/sg-gov-skills. It costs 236 tokens per session (2,958 once invoked), scanned A, original, MIT.

A security guide for generative AI features under Singapore's ICT&SS Policy Reform. Generative AI means software that creates text or other content from input, including chatbots, agents, and retrieval-augmented systems.

In plain words
What is it for?
Use it when building or reviewing an application that calls an AI model, hosts model weights, uses a RAG system, or provides a chatbot or agent.
Why use it?
It helps determine what sensitive data and models a feature may use, and how to address model provenance, input safety, output checking, and user awareness.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cybersecurity plugin — 10 skills shipped together

not rated 17repo 7d ago A scan 236 tokens original MIT

Good fit Use it when building or reviewing an application that calls an AI model, hosts model weights, uses a RAG system, or provides a chatbot or agent.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security
View source ↗ kyleoliveiro/sg-gov-skills
Install

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.

Any agent
npx skills add kyleoliveiro/sg-gov-skills --skill sg-gov-gen-ai-security
Clone the repo
git clone --depth 1 https://github.com/kyleoliveiro/sg-gov-skills

Made for: Claude Code.

Or install cybersecurity, the plugin that ships this one along with the rest of its 10 skills.

Wrote 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.

agentmods badge for sg-gov-gen-ai-security

README.md
[![agentmods](https://agentmods.dev/badge/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security/github.svg)](https://agentmods.dev/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security)
Your own site
<a href="https://agentmods.dev/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security"><img src="https://agentmods.dev/badge/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security/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.

agentmods 80×15 button for sg-gov-gen-ai-security

Your own site · 80×15
<a href="https://agentmods.dev/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security"><img src="https://agentmods.dev/badge/skills/kyleoliveiro/sg-gov-skills/sg-gov-gen-ai-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 236 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,958 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00236 $0.02958
Opus 5.5 $0.00094 $0.01183
Sonnet 5.5 $0.00047 $0.00592
Haiku 4.5 $0.00024 $0.00296

Measured 7d ago against content hash 27c3c45423fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sg-gov-gen-ai-security 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 7d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (evals/files/app.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/sg-gov-gen-ai-security/SKILL.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sg-gov-gen-ai-security: GA controls for SG government GenAI features

You are building or auditing a Generative AI feature against the ICT&SS Policy Reform Gen-AI SSP overlay: the 8 GA controls (GA-1..GA-8) plus DP-8, nine controls in total. The family's scope is securing the use of GenAI models and applications: where the model runs, what data may reach it, the provenance of self-hosted weights, input safeguards, output evaluation, and user awareness. Most of these are design decisions made at the start, and an audit or VAPT of a GenAI service checks them.

The maximum data classification the feature handles determines which model/provider you may call. Settle that first; the other controls depend on it.

What this family is not about

The GA controls are about data-classification governance, provider agreements, weight provenance, input safeguards, output evaluation, and user awareness, not primarily prompt-engineering defenses. Two things people wrongly expect here:

  • Prompt injection / jailbreak defense has no separately named GA control. It is tested under GA-7 (safety) and fixed as Application Security (sg-gov-secure-coding-as). Cover it, but don't cite a non-existent "GA prompt-injection control."
  • The provider API key is an application secret (AS-8, sg-gov-secure-coding-as), not GA-3. GA-3 governs the provider's contract, not how you store the key.

Source and currency

Control text in this skill and references/ga-controls.md is embedded from info.standards.tech.gov.sg as of 2026-07-22; the pages were last updated 24 March 2026. The standards iterate actively. For any compliance-critical decision, verify against the live pages:

Reference files

  • references/ga-controls.md — full text of all 8 GA controls + DP-8 (statement, recommendations, risk, the two GA-5 parameters), data-classification background, and cross-family notes. Read it when you need exact wording, e.g. for an audit response or SSP documentation.
  • references/implementation-recipes.md — concrete, framework-neutral code and config per control: provider-routing by classification, the GA-3 agreement checklist, safetensors / approved-loader loading, file-upload safeguards, DP-8 input disclosure, the GA-7 eval harness, and the GA-8 acknowledgment gate. Read it when building.

Read the full file on GitHub · 194 lines

Files

What ships with it

7 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.

Changes

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.

  1. 7d ago First seen · 194 lines · 236 tokens per session scan A 27c3c45423fd

Subscribe to this mod's changes

sg-gov-gen-ai-security is a skill published in the GitHub repository kyleoliveiro/sg-gov-skills (17 stars, last pushed 7d ago), licensed MIT. It adds 236 tokens to every session and 2,958 once invoked, about $0.0009 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 2026-11-03.

Data and AIRAG and vectorsSecurity
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