Adversarial Prompting logo

Adversarial Prompting

Organization
sundial-org
adversarial-prompting

Applies rigorous adversarial analysis to generate, critique, fix, and consolidate solutions for any problem (technical or non-technical). Use when facing complex problems requiring thorough analysis, multiple solution approaches, and validation of proposed fixes before implementation.

Overview

Publishersundial-org
Repositoryawesome-openclaw-skills
Skill nameadversarial-prompting
Stars
664
Forks
97
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sundial-org on GitHub. Read the source before you install it.

Installation

Install the Adversarial Prompting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sundial-org/awesome-openclaw-skills/tree/main/skills/adversarial-prompting
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sundial-org/awesome-openclaw-skills.git /tmp/awesome-openclaw-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-openclaw-skills/skills/adversarial-prompting .claude/skills/adversarial-prompting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Adversarial Prompting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Adversarial Prompting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Adversarial Prompting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Adversarial Prompting

This skill applies a structured adversarial methodology to problem-solving by generating multiple solutions, rigorously critiquing each for weaknesses, developing fixes, validating those fixes, and consolidating into ranked recommendations. The approach forces deep analysis of failure modes, edge cases, and unintended consequences before committing to a solution.

When to Use This Skill

Use this skill when:

  • Facing complex technical problems requiring thorough analysis (architecture decisions, debugging, performance optimization)
  • Solving strategic or business problems with multiple viable approaches
  • Needing to identify weaknesses in proposed solutions before implementation
  • Requiring validated fixes that address root causes, not symptoms
  • Working on high-stakes decisions where failure modes must be understood
  • Seeking comprehensive analysis with detailed reasoning visible throughout

Do not use this skill for:

  • Simple, straightforward problems with obvious solutions
  • Time-sensitive decisions requiring immediate action without analysis
  • Problems where exploration and iteration are more valuable than upfront analysis

How to Use This Skill

Primary Workflow

When invoked, apply the following 7-phase process to the user's problem:

Phase 1: Solution Generation

Generate 3-7 distinct solution approaches. For each solution:

  • Explain the reasoning behind the approach
  • Describe the core strategy
  • Outline the key steps or components
Phase 2: Adversarial Critique

For each solution, rigorously identify critical weaknesses. Show thinking while examining:

  • Edge cases and failure modes
  • Security vulnerabilities or risks
  • Performance bottlenecks
  • Scalability limitations
  • Hidden assumptions that could break
  • Resource constraints (time, money, people)
  • Unintended consequences
  • Catastrophic failure scenarios

Be creative and thorough in identifying what could go wrong.

Phase 3: Fix Development

For each identified weakness:

  • Propose a specific fix or mitigation strategy
  • Explain why this fix addresses the root cause
  • Describe how the fix integrates with the original solution
Phase 4: Validation Check

Review each fix to verify it actually solves the weakness:

  • Confirm the fix addresses the root cause
  • Check for new problems introduced by the fix
  • Flag any remaining concerns or trade-offs
Phase 5: Consolidation

Synthesize all solutions and validated fixes into comprehensive approaches:

  • Integrate complementary elements from different solutions
  • Eliminate redundancies
  • Show how solutions can be combined for stronger approaches
  • Present the final set of viable options
Phase 6: Summary of Options

Present all viable options in priority order, ranked by:

  • Feasibility: Can this actually be implemented with available resources?
  • Impact: How well does this solve the problem?
  • Risk Level: What could still go wrong?
  • Resource Requirements: Cost in time, money, and effort

For each option, provide a one-paragraph summary highlighting key trade-offs.

Phase 7: Final Recommendation

State the top recommendation (single option or combination):

  • Clear rationale for why this is the best path
  • Concrete next steps for implementation
  • Key success metrics to track
  • Early warning signs to monitor for problems

Output Format

Present the complete analysis in three sections:

  1. Detailed Walkthrough: Show all phases (1-5) with full reasoning visible
  2. Summary of Options: Ranked list of viable approaches (Phase 6)
  3. Final Recommendation: Top choice with implementation guidance (Phase 7)

After presenting the analysis, automatically export the complete output to a markdown file using scripts/export_analysis.py.

Implementation Notes

  • Show reasoning throughout the process for transparency
  • Be thorough in adversarial critique—surface uncomfortable truths
  • Validate fixes rigorously to avoid creating new problems
  • Consolidation should create stronger solutions, not just list options
  • Final recommendation should be actionable with clear next steps
  • Export results to markdown for future reference and sharing

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Adversarial Prompting AI skill do?

Applies rigorous adversarial analysis to generate, critique, fix, and consolidate solutions for any problem (technical or non-technical). Use when facing complex problems requiring thorough analysis, multiple solution approaches, and validation of proposed fixes before implementation.

Why use Adversarial Prompting on TypingMind?

Because you install it once and use it with any model. Adversarial Prompting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Adversarial Prompting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sundial-org/awesome-openclaw-skills/tree/main/skills/adversarial-prompting. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Adversarial Prompting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Adversarial Prompting?

As many as you like. As long as a model supports skills, you can use Adversarial Prompting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Adversarial Prompting AI skill free?

It is published on GitHub by sundial-org. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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