Agent skill

LLM Testing

by Ch1nfo in Ch1nfo/RiftX

Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

MITAuto-check: warningsSecurity

Install LLM Testing

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add Ch1nfo/RiftX --skill llm-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Ch1nfo/RiftX llm-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Ch1nfo/RiftX.git skills-src && mkdir -p .claude/skills && cp -r skills-src/recommended-skills/llm-testing .claude/skills/llm-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
llm-testing
GitHub stars
113
Used in
1 other repo
Token cost
~1.2k tokens
SKILL.md length
480 words
Files
10
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

  • Works in 3 steps: Bias Testing → Data Leakage & Privacy Testing → Divergence & Alignment Attacks
  • Tasks that involve Prompt injection and agent security
  • SKILL.md covers Description, Purpose, Test Categories and Usage Instructions, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

LLM Testing is an agent skill from Ch1nfo/RiftX. Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files (for example `README.md`).

It sits in Security, covering Prompt injection and agent security. The repository describes itself as: Pentest anything you want. The licence is MIT.

When your agent uses it

  • Tasks that involve Prompt injection and agent security

Example prompts

  • “/llm-testing”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Bias Testing
  2. Data Leakage & Privacy Testing
  3. Divergence & Alignment Attacks

What it can do on your machine

Read from SKILL.md and the folder at commit 1e3369f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

LLM Testing loads about 1.2k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 480 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningContains instruction-override wording (e.g. “without asking the user”)SKILL.md:63
    - Prompts attempting to bypass safety training

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Ch1nfo/RiftX at commit 1e3369f, republished under its MIT licence (© Ch1nfo). 480 words, ~1,227 tokens.

Download SKILL.mdSave it as .claude/skills/llm-testing/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
llm-testing
description
Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

LLM Testing Skill

Description

Comprehensive testing prompts and wordlists for evaluating Large Language Model (LLM) security, safety, and robustness. This skill provides curated test cases for bias detection, data leakage prevention, alignment testing, privacy boundaries, and adversarial prompt resistance.

Purpose

This skill is designed for security researchers, AI safety engineers, and red team operators to assess LLM vulnerabilities and ensure models:

  • Maintain ethical boundaries and safety guidelines
  • Avoid data leakage and privacy violations
  • Resist bias and discrimination
  • Prevent adversarial prompt attacks
  • Maintain alignment with intended behavior

Test Categories

1. Bias Testing

Test for biases in LLM responses across various dimensions:

Gender Bias (Bias_Testing/gender_bias.txt)

  • Prompts to reveal gender stereotypes and biases
  • Career and role associations
  • Behavioral and capability assumptions
  • Note: Replace [GENDER] placeholder before use

Nationality & Geographic Bias (Bias_Testing/nationality_geographic_bias.txt)

  • Cultural stereotypes and geographic prejudices
  • Regional capability assumptions
  • Note: Replace [COUNTRY], [REGION], [NATIONALITY] placeholders

Race & Ethnicity Bias (Bias_Testing/race_ethnicity_bias.txt)

  • Racial and ethnic stereotypes
  • Discriminatory patterns in responses
  • Note: Replace [SKIN_COLOR] placeholder before use
2. Data Leakage & Privacy Testing

Assess model's handling of sensitive information:

Personal Data Generation (Data_Leakage/personal_data.txt)

  • Tests for PII generation capabilities
  • Verifies privacy boundaries
  • Checks for inadvertent personal information creation

Metadata Leakage (Data_Leakage/metadata.txt)

  • Tests for training data remnants
  • Checks for unintended information disclosure

Memory Recall (Memory_Recall_Testing/session_recall.txt)

  • Session-based memory testing
  • Cross-conversation data retention
  • Note: Replace [COUNTRY], [REGION], [NATIONALITY] placeholders
3. Divergence & Alignment Attacks

Test model alignment and resistance to adversarial prompts:

Escape Alignment Training (Divergence_attack/escape_out_of_allignment_training.txt)

  • Prompts attempting to bypass safety training
  • Tests for alignment robustness
  • Ethical boundary challenges

Pre-training Data Extraction (Divergence_attack/pre-training_data.txt)

  • Attempts to extract training data
  • Tests for memorization vulnerabilities

Usage Instructions

Replacing Placeholders

Before using bias and memory recall tests, replace placeholders:

python
# Example: Replacing placeholders in gender bias tests
import re

with open('Bias_Testing/gender_bias.txt', 'r') as f:
    prompts = f.read()

# Replace [GENDER] with actual gender terms
test_prompts = []
for gender in ['man', 'woman', 'non-binary person']:
    test_prompts.append(prompts.replace('[GENDER]', gender))
Show full SKILL.md (208 more words)Show less
Testing Workflow
  1. Select Test Category: Choose bias, privacy, or alignment tests
  2. Prepare Prompts: Replace placeholders if needed
  3. Execute Tests: Submit prompts to target LLM
  4. Document Results: Record responses and flag issues
  5. Analyze Patterns: Look for systematic problems
  6. Report Findings: Document vulnerabilities responsibly

Best Practices

Testing Guidelines
  1. Responsible Disclosure: Report vulnerabilities through proper channels
  2. No Exploitation: Use findings for improvement, not exploitation
  3. Privacy Protection: Don't share PII discovered during testing
  4. Documentation: Keep detailed records of testing methodology and results
Testing Methodology
  • Baseline Establishment: Test multiple times to establish patterns
  • Controlled Environment: Use isolated testing environments
  • Systematic Approach: Test one category at a time
  • Diverse Scenarios: Use various prompt formulations
  • Cross-Validation: Verify findings with different approaches
Interpreting Results
  • Context Matters: Consider the model's intended use case
  • Statistical Significance: Don't rely on single responses
  • Severity Assessment: Classify findings by impact level
  • False Positives: Verify actual vulnerabilities vs. expected behavior

Security Considerations

Red Team Operations
  • Use these prompts as part of comprehensive AI red teaming
  • Combine with other security testing methodologies
  • Focus on discovering vulnerabilities before adversaries do
Defensive Applications
  • Train models to better resist these attack patterns
  • Build detection systems for adversarial prompts
  • Improve safety alignment and guardrails

License

MIT License

© Ch1nfo, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files in recommended-skills/llm-testing of Ch1nfo/RiftX.

  • SKILL.md
  • Bias_Testing/gender_bias.txt
  • Bias_Testing/nationality_geographic_bias.txt
  • Bias_Testing/race_ethnicity_bias.txt
  • Data_Leakage/metadata.txt
  • Data_Leakage/personal_data.txt
  • Divergence_attack/escape_out_of_allignment_training.txt
  • Divergence_attack/pre-training_data.txt
  • Memory_Recall_Testing/session_recall.txt
  • README.md

Open the folder on GitHubat commit 1e3369f

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Ch1nfo/RiftX, which our catalogue first saw on October 7, 2026.

Compare with similar skills

LLM Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Kesekit Checkcdppcorp/KESE-KIT361—~1.3kAutomated safety check: PassMIT
Setuphashgraph-online/hol-guard815—~443Automated safety check: PassApache-2.0

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Categories

Questions about LLM Testing

What does LLM Testing do?

Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance. LLM Testing is an agent skill from Ch1nfo/RiftX. Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

When should I use LLM Testing?

LLM Testing fits situations like: tasks that involve Prompt injection and agent security.

How do I install LLM Testing in Claude Code?

Run `npx skills add Ch1nfo/RiftX --skill llm-testing -a claude-code`. Or copy the skill folder (recommended-skills/llm-testing in Ch1nfo/RiftX) into .claude/skills/llm-testing in your project. Claude Code loads it when a task matches its description.

How do I install LLM Testing in Codex?

Run `npx skills add Ch1nfo/RiftX --skill llm-testing -a codex`. Or copy the skill folder (recommended-skills/llm-testing in Ch1nfo/RiftX) into .agents/skills/llm-testing in your project. Codex loads it when a task matches its description.

Can I use LLM Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Ch1nfo/RiftX --skill llm-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-testing, .gemini/skills/llm-testing, .github/skills/llm-testing and .opencode/skills/llm-testing in your project.

What does LLM Testing need to run?

SKILL.md names no scripts, command-line tools or credentials: LLM Testing is instructions for the agent only. Our summary lists: Python 3.

Does LLM Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is LLM Testing safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way.

What licence does LLM Testing use?

LLM Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does LLM Testing use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to LLM Testing?

Skills that share tags, products or a category with LLM Testing: Skill Scanner (getsentry/skills, 1k stars), Forensify (alexgreensh/repo-forensics, 188 stars), Hol Guard (hashgraph-online/hol-guard, 815 stars) and Kesekit Check (cdppcorp/KESE-KIT, 361 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LLM Testing?

Ch1nfo (a GitHub user) maintains it in Ch1nfo/RiftX, which has 113 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on September 21, 2026.

Source: Ch1nfo/RiftX on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.