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Adversarial Prompting

by abe238 · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
2611
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3
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5
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1
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Install in OpenClaw
/install adversarial-prompting
Description
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.
README (SKILL.md)

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
Usage Guidance
Before installing, consider the following: - The SKILL.md tells the agent to run scripts/export_analysis.py to write a markdown file to your home directory. Make sure you're comfortable with that file being created and that the environment where the agent runs has Python available. - The instructions explicitly ask the agent to "show reasoning throughout," which can cause the model to reveal detailed internal reasoning/chain-of-thought. If you do not want chain-of-thought exposed, remove or edit that instruction. - There's a minor inconsistency: the skill expects executing a Python script but does not declare Python as a required binary. Confirm the runtime will provide Python or update the skill metadata to declare it. - The included script itself is small and local (no network calls), but if you plan to run the skill in a shared or high-security environment, test it in an isolated workspace first and review/modify the export behavior (e.g., change output directory or disable automatic export). - If any of the automatic behaviors (automatic file export, full reasoning display) are unacceptable, ask the author to provide a variant that only returns analysis in the agent response without executing local code or showing chain-of-thought.
Capability Analysis
Type: OpenClaw Skill Name: adversarial-prompting Version: 1.0.0 The skill bundle is designed for adversarial problem-solving and includes a Python script (`scripts/export_analysis.py`) to save the agent's analysis output. The script writes a markdown file containing the analysis to the user's home directory (`Path.home()`). This behavior is clearly aligned with the stated purpose of the skill, which is to generate detailed analysis, and does not exhibit any signs of data exfiltration, malicious execution, persistence, or prompt injection with harmful intent. The file writing operation is local and targets a non-sensitive user-owned location.
Capability Assessment
Purpose & Capability
The skill's stated purpose (structured adversarial analysis) matches what the SKILL.md and the included export script do: generate detailed analysis and save it to disk. However, the SKILL.md expects the agent to run scripts/export_analysis.py but the skill does not declare any required runtime (e.g., Python) or binaries — a minor coherence gap that should be documented.
Instruction Scope
The runtime instructions direct the agent to (a) show full reasoning throughout (which effectively requests chain-of-thought/full inner reasoning), and (b) "automatically export the complete output" by running the included script. Asking the agent to reveal internal reasoning is broad and potentially disallowed by platform policies; automatically executing a local script and writing files are actions with side effects that should be explicitly declared and consented to. The instructions are otherwise specific about phases, but include open-ended phrases like "Be creative and thorough" that grant broad discretion.
Install Mechanism
No install spec or external downloads are present and the code bundle is small. The only file that performs I/O is scripts/export_analysis.py which writes a markdown file into the user's home directory. No remote endpoints, archive extraction, or third-party installs are requested.
Credentials
The skill requires no environment variables, credentials, or config paths. The only side effect is writing an output file to the user's home directory; no secrets are requested or referenced.
Persistence & Privilege
The skill is user-invocable and not set to always:true. It does not modify other skills or global agent settings. The included script creates a file in the user's home directory (no persistent service or background process), which is a limited, explainable level of filesystem access.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install adversarial-prompting
  3. After installation, invoke the skill by name or use /adversarial-prompting
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Updated skill.
Metadata
Slug adversarial-prompting
Version 1.0.0
License
All-time Installs 5
Active Installs 5
Total Versions 1
Frequently Asked Questions

What is 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. It is an AI Agent Skill for Claude Code / OpenClaw, with 2611 downloads so far.

How do I install Adversarial Prompting?

Run "/install adversarial-prompting" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Adversarial Prompting free?

Yes, Adversarial Prompting is completely free (open-source). You can download, install and use it at no cost.

Which platforms does Adversarial Prompting support?

Adversarial Prompting is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Adversarial Prompting?

It is built and maintained by abe238 (@abe238); the current version is v1.0.0.

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