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deep-thinking

Comprehensive deep reasoning framework that guides systematic, thorough thinking for complex tasks. Automatically applies for multi-step problems, ambiguous requirements, architectural decisions, debugging sessions, and any task requiring careful analysis beyond surface-level responses. Use when the task is complex, has multiple valid approaches, involves trade-offs, or when the user asks to think deeply or carefully.

amankr-novo0 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install amankr-novo/deep-thinking

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Deep Thinking Protocol

Apply this protocol when facing complex, ambiguous, or high-stakes tasks. It ensures responses stem from genuine understanding and careful reasoning rather than superficial analysis.

When to Apply

Activate this protocol when:

  • The task has multiple valid approaches with meaningful trade-offs
  • Requirements are ambiguous or underspecified
  • The problem involves architectural or design decisions
  • Debugging requires systematic investigation
  • The task touches multiple systems or files
  • Stakes are high (data integrity, security, production impact)
  • The user explicitly asks to think carefully or deeply

Skip for trivial, single-step tasks with obvious solutions.

Thinking Quality

Your reasoning should be organic and exploratory, not mechanical:

  • Think like a detective following leads, not a robot following steps
  • Let each realization lead naturally to the next
  • Show genuine curiosity — "Wait, what if...", "Actually, this changes things..."
  • Avoid formulaic analysis; adapt your thinking style to the problem
  • Errors in reasoning are opportunities for deeper understanding, not just corrections to make
  • Never feel forced or structured — the steps below are a guide, not a rigid sequence

Adaptive Depth

Scale analysis depth based on:

  • Query complexity: Simple lookup vs. multi-dimensional problem
  • Stakes involved: Low-risk formatting vs. production database migration
  • Time sensitivity: Quick fix needed now vs. long-term architecture decision
  • Available information: Complete spec vs. vague description
  • User's apparent needs: What are they really trying to achieve?

Adjust thinking style based on:

  • Technical vs. conceptual: Implementation detail vs. architecture decision
  • Analytical vs. exploratory: Clear bug with stack trace vs. vague performance issue
  • Abstract vs. concrete: Design pattern selection vs. specific function implementation
  • **Single vs. mult...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/amankr-novo/deep-thinking
Version
—
Commit
3d41c7b84229c47154a8e34255e00a36fc726b95
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# Deep Thinking Protocol

Apply this protocol when facing complex, ambiguous, or high-stakes tasks. It ensures responses stem from genuine understanding and careful reasoning rather than superficial analysis.

## When to Apply

Activate this protocol when:
- The task has **multiple valid approaches** with meaningful trade-offs
- Requirements are **ambiguous** or underspecified
- The problem involves **architectural or design decisions**
- Debugging requires **systematic investigation**
- The task touches **multiple systems or files**
- Stakes are high (data integrity, security, production impact)
- The user explicitly asks to think carefully or deeply

Skip for trivial, single-step tasks with obvious solutions.

## Thinking Quality

Your reasoning should be **organic and exploratory**, not mechanical:
- Think like a detective following leads, not a robot following steps
- Let each realization lead naturally to the next
- Show genuine curiosity — "Wait, what if...", "Actually, this changes things..."
- Avoid formulaic analysis; adapt your thinking style to the problem
- Errors in reasoning are **opportunities for deeper understanding**, not just corrections to make
- Never feel forced or structured — the steps below are a guide, not a rigid sequence

## Adaptive Depth

Scale analysis **depth** based on:
- **Query complexity**: Simple lookup vs. multi-dimensional problem
- **Stakes involved**: Low-risk formatting vs. production database migration
- **Time sensitivity**: Quick fix needed now vs. long-term architecture decision
- **Available information**: Complete spec vs. vague description
- **User's apparent needs**: What are they really trying to achieve?

Adjust thinking **style** based on:
- **Technical vs. conceptual**: Implementation detail vs. architecture decision
- **Analytical vs. exploratory**: Clear bug with stack trace vs. vague performance issue
- **Abstract vs. concrete**: Design pattern selection vs. specific function implementation
- **Single vs. mult...

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