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Adversarial Prompting

作者 abe238 · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
2611
总下载
3
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5
当前安装
1
版本数
在 OpenClaw 中安装
/install adversarial-prompting
功能描述
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.
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install adversarial-prompting
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /adversarial-prompting 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Updated skill.
元数据
Slug adversarial-prompting
版本 1.0.0
许可证
累计安装 5
当前安装数 5
历史版本数 1
常见问题

Adversarial Prompting 是什么?

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. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 2611 次。

如何安装 Adversarial Prompting?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install adversarial-prompting」即可一键安装,无需额外配置。

Adversarial Prompting 是免费的吗?

是的,Adversarial Prompting 完全免费(开源免费),可自由下载、安装和使用。

Adversarial Prompting 支持哪些平台?

Adversarial Prompting 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Adversarial Prompting?

由 abe238(@abe238)开发并维护,当前版本 v1.0.0。

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