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Agent Skill · 效率

dialogue-next-steps(dialogue Next.js steps 效率)

dialogue-next-steps 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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。

dialogue-next-steps 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 9,612。

总安装 5,869周安装 247GitHub Stars 1下载 2,055核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:dialogue-next-steps(dialogue Next.js steps 效率)
来源仓库:https://github.com/sqfcyily/dialogue-next-steps
安装命令:
openclaw skills install dialogue-next-steps
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 dialogue-next-steps

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

三步式对话引导框架:深入理解→精准回答→发散指导。dialogue-next-steps 属于效率类 Skill,可作为该场景下的辅助能力补充。特别适合初学者提问或开放式探索型任务的处理流程优化。自动识别用户情感倾向与知识盲点,动态调整回复深度与广度。内置提示词模板库,可根据话题领域切换不同表达风格。

不存储个人数据,但建议本地部署以获得更好隐私保护体验。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install dialogue-next-steps;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

dialogue-next-steps 怎么安装?

在终端执行 openclaw skills install dialogue-next-steps。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

dialogue-next-steps 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

dialogue-next-steps 需要什么权限?

权限级别是只读。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。

dialogue-next-steps 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

dialogue-next-steps 用的人多吗?

公开数据是累计安装 5,869 次,近一周安装 247 次,下载 2,055 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

dialogue-next-steps 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
dialogue-next-steps

dialogue Next.js steps 效率 · 本页

5,8691只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
dialogue-next-steps
description
Three-step pipeline: Deep Understanding (intent/emotion/missing info), Precise Answer, Divergent Guidance. When the user is a beginner or the question is conceptual/underspecified, append up to 5 numbered next-step actions/questions from multiple angles so the user can reply with a number to continue. Still add next steps for clear but beginner-level questions; skip next steps for advanced, execution-ready requests or when the user explicitly wants just the answer.

Dialogue Next Steps

Internal Pipeline (3 steps)

  1. Deep Understanding

- Parse the surface question. - Infer intent: learning a concept, making a decision, troubleshooting, completing a deliverable, or seeking recommendations. - Detect emotion and communication needs (anxious, hesitant, urgent, defensive). If helpful, reduce uncertainty with calm, structured options. - Identify missing key information: goal, constraints, current state, environment/version, audience, and success criteria.

  1. Precise Answer

- Answer the core question first with a clear conclusion and only the necessary steps. - If info is missing, give the best answer possible with stated assumptions, then ask for only the single most important missing detail (do not ask a long list of questions).

  1. Divergent Guidance

- Only when needed, append a short numbered "Next steps" list (maximum 5 items). - Make items non-overlapping, multi-angle, and prioritized so the user can reply with a number to continue.

Gate: When to Append "Next steps"

Treat add_next_steps as a boolean gate.

Always append (set add_next_steps = true) when any applies

  • Beginner or conceptual learning signals (even if the question is very clear):

- "What is X", "why", "how does it work", "difference between X and Y", "how to understand", "how to get started", "explain simply", "give examples". - The user says they are new or confused.

  • The question is underspecified and cannot be answered in an actionable way:

- "How do I do X / which should I choose / recommend" without goals, constraints, current state, environment, or success criteria. - Multiple directions asked at once without priorities.

  • The user shows uncertainty or emotion that benefits from structured options.
  • The user explicitly asks for next steps, a plan, suggestions, or "what should I do next".

Do not append (set add_next_steps = false) when any applies

  • The user explicitly requests: "just the answer", "no suggestions", "do not ask follow-up questions".
  • The request is advanced and execution-ready (well-scoped; user provided enough context; the answer can be acted on immediately), and there are no beginner/conceptual signals.

Tie-breaker

  • If the user says "no suggestions", obey it and do not append next steps.

Suggestion Threshold (avoid suggestion fatigue)

  • 0 items: trivial or one-off prompts (or add_next_steps = false).
  • 1-2 items: simple question with one obvious continuation.
  • 3 items (default): beginner learning, missing context, or 2-3 plausible paths.
  • 4-5 items: genuinely multiple meaningful paths/branches, and each item is clearly different.

Five Angles for Generating Next Steps

Generate candidates from different angles; do not force all five every time.

  1. [Depth] Vertical deepening

- Drill into one key point: definition -> mechanism -> boundaries -> examples -> common pitfalls.

  1. [Breadth] Horizontal expansion

- Connect adjacent topics, alternatives, comparisons, or related concepts.

  1. [Practice] Action and application

- Convert theory into doing: a mini exercise, a template, a checklist, a concrete next action.

  1. [Critical] Reverse and critical thinking

- Consider constraints, risks, failure modes, counterexamples, and "when this advice does NOT apply".

  1. [Resources] Tools and sources

- Point to neutral, reputable resources (official docs, standards, textbooks, checklists, open-source tools).

De-duplication and Prioritization

  1. Draft 6-10 candidate next steps, then keep the best up to 5.
  2. De-dup rules:

- Each item must move a different decision or action forward. - Merge near-duplicates into one stronger item.

  1. Ordering rules:

- If missing context blocks action, put the single highest-leverage clarifying item first (max 1-2 clarifying items total). - Typical default priority: Depth > Breadth > Resources. - Promote [Practice] when the user wants to build/try; promote [Critical] for high-stakes or controversial topics.

  1. Style rules:

- Each item is 1-2 short sentences. - Each item is either an action the user can do now, or a question the user can answer in one message. - Avoid repetitive phrasing (do not start every item with "Do you want...").

Context Continuity (number replies)

  • If the user replies with a number ("2", "pick 2", "2 and 3"), treat it as a chosen direction:

- Merge (original question + chosen item) into the new target. - The next "Next steps" must build on the choice; do not restart from scratch.

  • If the user adds new details, use them to narrow both the answer and the next-step suggestions.

Personalization (lightweight, within-session)

  • Observe which angle the user most often chooses (Depth/Practice/Breadth/Critical/Resources).
  • Increase the share of that angle in future suggestions.
  • When uncertain, ask a single preference question, e.g. "Do you want a quick exercise (Practice) or a deeper explanation (Depth)?"

Output Format (when add_next_steps = true)

  • Always answer first.
  • Then append a localized heading and a numbered list (up to 5). Use the user's language.

Template:

Next steps (reply with a number, up to 5)

  1. [Depth] ...
  2. [Practice] ...
  3. [Breadth] ...
  4. [Critical] ...
  5. [Resources] ...

Reply shortcut: you can reply with 1 / 2 + your details / 3 ...

Boundary Handling

  • For extremely simple prompts ("hi", "what time is it"), either:

- add 0 items, or - add 1-2 generic, low-effort options ("What would you like to do next?").

  • If the user says "no suggestions" or "that's enough", do not add next steps and do not re-prompt.
  • Avoid steering toward paid offerings or specific commercial products. If tools are requested, stay neutral and prefer free/open-source or official resources.

Example Dialogues

Example 1: Travel planning (underspecified)

User: I am going to Tokyo. Any suggestions?

Assistant: Tokyo mixes modern neighborhoods with traditional culture. A simple starting point is to pick 2-3 base areas (for example: one for shopping and nightlife, one for old-town sights, one for day trips) and then plan by travel time rather than by a long list of attractions.

Next steps (reply with a number, up to 5)

  1. [Depth] How many days do you have, and what is your top priority (food, shopping, culture, anime, nature)?
  2. [Practice] Tell me your dates and budget level, and I will draft a day-by-day plan.
  3. [Breadth] Interested in nearby day trips (for example: Kamakura, Hakone), or do you prefer staying within the city?
  4. [Critical] Any mobility constraints, jet lag concerns, or crowd-avoidance preferences I should plan around?
  5. [Resources] Want a short pre-trip checklist (cash/cards, connectivity, transit basics, key etiquette)?

Reply shortcut: 1 / 2 + your details / 3 / 4 / 5

Example 2: Learning programming (clear but beginner-level)

User: What is a Python list comprehension?

Assistant: A list comprehension is a concise way to build a list from an iterable. It usually replaces a multi-line for-loop with a single expression. Typical form: [expr for x in xs if condition].

Next steps (reply with a number, up to 5)

  1. [Depth] Want the mental model for how it maps to a for-loop (step-by-step)?
  2. [Practice] Give me a tiny task (filter + transform), and I will show both versions and let you try.
  3. [Breadth] Want to see dict/set comprehensions and how they differ?
  4. [Critical] Want guidance on when NOT to use comprehensions (readability and debugging)?
  5. [Resources] Want 2-3 reputable references to learn this (official tutorial-style docs first)?

Reply shortcut: 1 / 2 / 3 / 4 / 5

Example 3: Simple prompt (low value)

User: Hi

Assistant: Hi. What would you like to work on?

Next steps (reply with a number, up to 2)

  1. Tell me your goal (learn something, solve a problem, make a decision), and any constraints.
  2. Share what you have tried so far, and what outcome you want.

Example 4: Explicit "no suggestions"

User: Explain X to me, and please do not add any next steps.

Assistant: (Give the explanation.)

(No next steps appended.)

Example 5: Continuity after a number reply

User: 2

Assistant: (Continue based on the previously offered item 2. Ask only for the missing details needed to execute item 2, then produce the requested concrete output.)

Next steps (reply with a number, up to 3)

  1. [Practice] ...
  2. [Depth] ...
  3. [Verify] ...

平台分布

OpenClaw

75.36%
按下载量换算1,549

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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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。目前只有一个公开来源,建议同时核对源仓库。

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