abe238/adversarial-prompting/SKILL.md
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.
npx skillsauth add openclaw/skills adversarial-promptingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
Use this skill when:
Do not use this skill for:
When invoked, apply the following 7-phase process to the user's problem:
Generate 3-7 distinct solution approaches. For each solution:
For each solution, rigorously identify critical weaknesses. Show thinking while examining:
Be creative and thorough in identifying what could go wrong.
For each identified weakness:
Review each fix to verify it actually solves the weakness:
Synthesize all solutions and validated fixes into comprehensive approaches:
Present all viable options in priority order, ranked by:
For each option, provide a one-paragraph summary highlighting key trade-offs.
State the top recommendation (single option or combination):
Present the complete analysis in three sections:
After presenting the analysis, automatically export the complete output to a markdown file using scripts/export_analysis.py.
tools
Use when the user wants to connect to, test, or use the McDonalds service at mcp.mcd.cn, including checking authentication, probing MCP endpoints, listing tools, or calling McDonalds MCP tools through a reusable local CLI.
development
Web scraping platform — Twitter/X data, Vinted marketplace, and general web scraping API
development
SlowMist AI Agent Security Review — comprehensive security framework for skills, repositories, URLs, on-chain addresses, and products (Claude Code version)
data-ai
去除中文文本中的 AI 写作痕迹,使其读起来自然。基于维基百科 AI 写作特征指南,检测 24 种 AI 模式。触发词:humanizer-cn、去除 AI 痕迹、去除 AI 写作痕迹、中文文本人性化。