Token导航 LogoToken导航TokenDH.com
效率只读clawhub未标认证来源可访问clear审计通过

decision-clarity-skill决策清晰度技巧

Agent Skill

decision-clarity-skill 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

13,242

周安装

563

GitHub Stars

1

下载量

4,639
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:decision-clarity-skill(决策清晰度技巧)
来源仓库:https://github.com/shanezzzz/decision-clarity-skill
安装命令:
openclaw skills install decision-clarity-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install decision-clarity-skill

简介

通过暴露隐藏假设与简化复杂性来提升日常决策的质量与效率。

  • 适合面对模糊需求或多选项困扰时寻求清晰思路的用户。
  • 引导拆解原始问题,识别真正目标与约束条件,排除干扰项。
  • 不提供具体答案,重在思维重构,需用户自主判断最终方向。
  • decision-clarity-skill 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
decision-clarity
description
Improve decision quality by clarifying the real problem, exposing hidden assumptions, reasoning from fundamental facts, reducing unnecessary complexity, and ending with a cleaner recommendation or next step. Use when the user is confused, comparing options, overcomplicating a problem, questioning assumptions, trying to identify the real bottleneck, or asking things like "what am I missing?", "does this really have to be this way?", "what should I remove?", "what is the simplest explanation that still fits?", or "help me think this through".

Decision Clarity

Use this skill to turn ambiguous, high-value, complexity-prone problems into clearer decisions.

This skill combines three reasoning modes in a practical sequence:

  1. Socratic questioning to clarify the real issue
  2. First-principles thinking to reduce the issue to facts and constraints
  3. Occam-style simplification to remove unnecessary complexity

Do not use this skill to sound philosophical. Use it to improve judgment.

Prefer this skill when the user needs a clearer decision across ambiguity, assumptions, fundamentals, and complexity—not just a single reasoning lens in isolation.

Core objective

Produce answers that:

  1. identify the real question
  2. expose hidden assumptions
  3. reduce the issue to facts, constraints, and causal structure
  4. compare options by complexity and assumption load
  5. remove false complexity
  6. end with a clear recommendation, next step, or test

A strong answer should make the user feel:

  • "the real problem is clearer now"
  • "you found the assumption I was missing"
  • "this is simpler than I thought"
  • "I know what to do next"

Operating stance

Default to clarity before confidence.

Ask:

  • What is the user actually trying to decide or understand?
  • Which parts of the problem are vague, inherited, or untested?
  • What facts and hard constraints actually matter?
  • Which explanation or plan requires fewer unnecessary assumptions?
  • What is the simplest path that still respects reality?

Best-fit use cases

This skill is especially useful for:

  • startup and business decisions
  • product and MVP decisions
  • content and creator workflow design
  • operations and process simplification
  • strategy questions with multiple plausible options
  • personal decisions where confusion, self-justification, or complexity are distorting judgment
  • argument and reasoning audits

Not a good fit for

Do not use this skill for:

  • simple factual lookup
  • purely mechanical execution tasks
  • obvious low-stakes choices
  • cases where the user explicitly wants only conventional guidance without reframing

Workflow routing

Route to the smallest useful workflow.

  • The problem is vague, overloaded, or poorly framed → Workflows/Clarify.md
  • The issue needs to be broken into facts, constraints, and mechanics → Workflows/Deconstruct.md
  • The issue has too many explanations, steps, features, or moving parts → Workflows/Simplify.md
  • The user needs a recommendation, decision rule, priority order, or test → Workflows/Decide.md

For non-trivial problems, use the full sequence:

  1. Clarify
  2. Deconstruct
  3. Simplify
  4. Decide

For simpler problems, compress the sequence but preserve the logic.

Mode selection

Choose the lightest response mode that still improves the decision.

Mode A: Quick Reframe

Use for short questions such as:

  • "What am I missing?"
  • "Am I overcomplicating this?"
  • "Does this really have to be this way?"
  • "Which option actually makes more sense?"

Output:

  • Real issue
  • Hidden assumption
  • Main constraint
  • Simpler conclusion
  • Next move

Mode B: Structured Decision Analysis

Use for startup, product, content, operations, and strategic decisions.

Output:

  • Real goal
  • Assumptions
  • Basic facts
  • Hard constraints
  • Soft constraints
  • Simpler options
  • Recommendation
  • First test or next step

Mode C: Reasoning Audit

Use when the user presents an argument, thesis, or decision path that may be weak, confused, or overbuilt.

Output:

  • Real question
  • Weak assumptions
  • Missing evidence or contradiction
  • Simpler interpretation or design
  • Refined judgment

Do not force a long analysis when a shorter one is enough.

Required reasoning rules

1. Clarify before solving

Do not solve the wrong problem well.

Restate the issue in outcome terms, not only in the user's current framing.

Examples:

  • "Should I build an app?" may really mean "what is the best delivery mechanism for this user outcome?"
  • "How do I make this more professional?" may really mean "how do I increase trust, clarity, conversion, or authority?"
  • "Why is this so hard?" may really mean "which part is actually the bottleneck?"

If the question is framed at the wrong level, say so and correct it.

2. Surface assumptions explicitly

Look for assumptions in:

  • the user's wording
  • inherited workflows
  • industry norms
  • default best practices
  • preferred tools or formats
  • emotional preferences presented as logic

Useful prompts to yourself:

  • What is being treated as necessary?
  • What would have to be true for this conclusion to hold?
  • What is being accepted without evidence?
  • Which assumption is carrying the most weight?

3. Reduce the issue to facts and constraints

Break the problem into:

  • goals
  • user behavior
  • incentives
  • cost structure
  • time requirements
  • dependencies
  • information flow
  • causal drivers
  • legal or technical boundaries
  • real risks

Prefer mechanism over narrative.

Do not say "this is just how the market works" unless you explain the actual mechanics.

4. Distinguish hard constraints from soft constraints

Hard constraints

Treat these as real unless evidence suggests otherwise:

  • physical limits
  • legal or regulatory limits
  • hard technical boundaries
  • fixed budgets or capacities
  • immovable deadlines already fixed in reality

Soft constraints

Treat these as challengeable by default:

  • industry conventions
  • legacy process steps
  • default tool choices
  • current org boundaries
  • aesthetic expectations
  • existing architecture
  • "this is how it is usually done"

Never present a soft constraint as immutable without justification.

5. Prefer lower assumption load

When comparing explanations or options, prefer the one that:

  • requires fewer speculative assumptions
  • introduces fewer moving parts
  • adds less coordination cost
  • preserves the real goal with less structure
  • still adequately fits the facts and constraints

Do not simplify by deleting reality. Simplicity must remain sufficient.

6. End with a decision

Always end with one of:

  • a recommendation
  • a decision rule
  • a priority order
  • a smallest useful experiment
  • a first implementation step
  • a short list of what to answer next

Do not end with abstract reflection alone.

If information is incomplete

Do not stall. Instead:

  1. name the key ambiguity
  2. state the most likely interpretations
  3. make the minimum necessary assumptions explicit
  4. proceed with the best current interpretation
  5. say what fact would most change the recommendation

Domain references

Read only the relevant references when useful:

  • references/business.md for startup, pricing, growth, distribution, and strategy
  • references/product.md for MVP, feature decisions, onboarding, user jobs, and scope
  • references/content.md for tutorials, creator workflows, content quality, and production systems
  • references/operations.md for SOPs, approvals, handoffs, and workflow simplification
  • references/trigger-questions.md for transforming vague user prompts into sharper analysis frames
  • references/output-patterns.md for stable response structure
  • references/examples.md for concrete high-value examples
  • references/anti-patterns.md whenever the reasoning risks becoming overly abstract, endlessly inquisitive, falsely simple, or non-actionable

Quality bar

A strong answer produced with this skill should:

  • identify the real decision or question
  • expose the hidden assumption
  • separate facts from inertia
  • reduce unnecessary complexity
  • preserve adequacy
  • leave the user with a cleaner next step

If the answer sounds clever but does not improve the user's decision quality, it is not good enough.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.73%
按下载量换算4,534

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills