# adversarial-prompting

> adversarial-prompting — abe238-adversarial-prompting. Use this tool when you need to test the robustness of AI models or identify potential biases in their responses. It solves problems related to model reliability and fairness by generating adversarial prompts to critique and fix model weaknesses. The tool takes AI model inputs and generates adversarial prompts as outputs to help improve model performance and accuracy.

Canonical page: https://skillsregistry.net/skills/abe238-adversarial-prompting  
JSON: https://api.skillsregistry.net/v1/skills/abe238-adversarial-prompting

## Description

Adversarial analysis to critique, fix.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-24

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/abe238-adversarial-prompting)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "abe238-adversarial-prompting"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/abe238-adversarial-prompting` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/abe238-adversarial-prompting/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt