# Genai Acceptance Review

> Review workflow for AI/LLM output usage to prevent over-trust, injection, and unsafe automation. Use when this capability is needed.

- Skill: `tomevault-io/genai-acceptance-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/genai-acceptance-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/genai-acceptance-review/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/genai-acceptance-review

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# GenAI Acceptance Review

## When to use

Use this skill when a system **consumes LLM output** to make decisions or perform actions.

## Inputs to collect (if available)

- What the model output is used for (advisory vs actionable)
- Tools/capabilities available to the system (file writes, network calls, deploys)
- Data entering prompts (PII/secrets? retrieved content sources?)
- Approval model (human-in-the-loop? step-up auth?)

## Threats to consider

- Prompt injection (content causes the model to ignore instructions)
- Over-trust / tool misuse (model output drives privileged actions)
- Data leakage (secrets/PII included in prompts or outputs)
- Indirect injection via retrieved content (RAG, web pages, PDFs)

## Step-by-step process

1. **Map the AI boundary**
   - Where prompts are built, where tools are called, what data enters/leaves.
2. **Classify outputs**
   - *Advisory*: suggestions for humans
   - *Actionable*: used by code to execute, write files, call APIs, change permissions
3. **Apply controls by class**
   - Advisory: disclaimers, human review, logging with redaction
   - Actionable: strict schema validation, allow-lists, capability gating, step-up approvals
4. **Prompt & retrieval hardening**
   - Separate system instructions from untrusted content
   - Use structured output (JSON schema) and reject invalid outputs
   - Limit context sources; sanitize retrieved content where possible
5. **Add misuse tests**
   - Include injection strings and verify they don’t trigger privileged actions
6. **Document safe usage**
   - Clear rules for what the model may decide vs what code must enforce

## Output

- Boundary diagram (textual is fine)
- Control recommendations (prevent/detect/respond)
- Test cases for injection and over-trust scenarios

## Repo integration (optional)

Related prompt:

- `check-for-unvalidated-genai-acceptances.prompt.md`

## Output format

- **Boundary map**: where untrusted content enters, where model output leaves
- **Threats**: top 5 with likelihood/impact
- **Controls**: prevent/detect/respond mapped to advisory vs actionable use
- **Validation**: misuse/prompt-injection test scenarios

## Examples

- “LLM suggests shell commands that CI executes” → require allow-listed command templates + schema validation + human approval for privileged operations.

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