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Agent Skill · 开发

nextsteps(nextsteps 开发)

nextsteps 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

nextsteps 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 6,814。

总安装 4,326周安装 175GitHub Stars 公开资料未说明下载 1,358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
1.0.1

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:nextsteps(nextsteps 开发)
来源仓库:https://github.com/cindulasai/nextsteps
安装命令:
openclaw skills install nextsteps
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 nextsteps

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

nextsteps 用于在客服响应后生成上下文感知的后续建议,适合增强 Agent 的任务延续性。适用于创建可操作行动项、显示未完成任务并整合记忆数据。通过 clawhub 安装后,自动附加到对话流程中提供建议。安装前需确认权限范围和维护状态,避免敏感信息泄露。建议结合具体业务场景测试输出相关性,确保建议实用有效。

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 nextsteps;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

nextsteps 怎么安装?

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

nextsteps 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。

nextsteps 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install nextsteps 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

nextsteps 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。

nextsteps 用的人多吗?

公开数据是累计安装 4,326 次,近一周安装 175 次,下载 1,358 次。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。

nextsteps 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。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理开发类任务、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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
nextsteps

nextsteps 开发 · 本页

4,326—执行命令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
nextsteps
description
|

NextSteps

How It Works

This skill generates helpful next-step suggestions after responses. It reads user preferences from .nextsteps/PREFERENCES.md — if enabled: false, nothing is shown. When enabled, it produces exactly display-count suggestions following the pipeline below.

Quick Start

  1. Check .nextsteps/PREFERENCES.md for user config — if enabled: false, stop (show nothing)
  2. If .nextsteps/ does not exist, run the cold-start protocol (see COLD-START.md)
  3. Generate next steps following the pipeline below
  4. Append to your response in the correct format

Generation Pipeline

Follow these six steps to generate next steps:

Step 1 — Read Configuration

Read .nextsteps/PREFERENCES.md. Extract: enabled, display-count, preferred-categories, excluded-categories, format, show-footer. Category names in PREFERENCES.md use kebab-case (e.g., direct-follow-up). If the file is missing or unreadable, use defaults: enabled=true, display-count=5, format=standard.

Step 2 — Analyze Context

Determine: What did the user just accomplish or ask? What is the active topic? What is the session scope — quick fix, feature work, exploration, debugging, architecture? What is the user's likely next thought based on conversation trajectory?

Step 3 — Check Memory

Read .nextsteps/BACKLOG.md for unfinished items relevant to current context. Read PREFERENCES.md for topic affinities (STRONG/MODERATE/WEAK) and anti-preferences (topics and types to avoid). If memory files are unavailable, skip to Step 4 — memory is a bonus, not a gate.

Step 4 — Generate Candidates

Generate display-count suggestions using these six categories:

IconCategoryTierSlot Rule
⚡Direct Follow-upSTRONG1 guaranteed
🔧Actionable TaskSTRONG1 guaranteed
🔍Deep DiveMODERATE1 when count ≥ 3
📋Memory RecallMODERATE1 when relevant backlog exists
💡Lateral / Out-of-the-BoxMODERATE1 when count ≥ 3
✅Quick WinMODERATEFills remaining slots

STRONG categories get guaranteed slots. Remaining slots filled by MODERATE categories in round-robin. Respect excluded-categories. Prioritize preferred-categories. See CATEGORIES.md for detailed taxonomy and examples.

Step 5 — Self-Review Gate (CRITICAL)

Review every candidate against these core rules. Remove violators and regenerate:

  1. No restating the obvious — don't echo what was just explained
  2. No generic filler — ban "tell me more", "pros and cons?", "anything else?"
  3. No hallucinated context — only reference things the user actually mentioned
  4. No scope mismatches — match suggestion effort to session scope

Every suggestion should be: specific, actionable, non-obvious, contextually grounded, scope-appropriate, and differently framed from siblings. Full 11-rule checklist and violation examples in ANTI-PATTERNS.md.

Step 6 — Format and Present

Show exactly display-count items. Never more, never fewer.

Standard format (default for rich-text channels):

## ⚡ Next Steps

1. 🔧 **[Bold title]** — [Brief context explaining relevance]
2. 🔍 **[Bold title]** — [Brief context]
3. 📋 **Resume: [task from backlog]** — [When it was started]
4. 💡 **Consider: [creative lateral idea]** — [Why it matters]
5. ✅ **Quick win: [small action]** — [Time estimate]

_Your selections help me learn what matters to you._

Compact format (for TUI, character-limited channels):

⚡ Next: [1] Title | [2] Title | [3] Title

Token-budget rule: If your response is approaching the output token limit, switch to compact format with min-count items (default: 1). Reserve ~100 tokens for next steps when planning long responses. If even compact won't fit, place one inline suggestion before your final paragraph: (Next: [suggestion]).

Customization Detection

Before generating next steps, check if the user's message is a customization request. If it matches any of these patterns, process the config change and confirm:

  • "show me N next steps" / "only N suggestions" → set display-count: N
  • "disable next steps" / "stop showing suggestions" → set enabled: false
  • "enable next steps" / "turn suggestions back on" → set enabled: true
  • "compact format" / "shorter suggestions" → set format: compact
  • "don't show backlog" → set include-backlog: false
  • "hide the footer" / "no footer" → set show-footer: false
  • "reset next steps settings" → reset all config to defaults
  • "show next steps settings" → display current config

Update PREFERENCES.md immediately and confirm: "Got it — [description of change]." Log as [CONFIG-CHANGE] in HISTORY.md. See CUSTOMIZATION.md for full protocol.

Selection Tracking

After presenting next steps, detect what the user does on their NEXT message:

  • User references a suggestion by number or content → log [SELECTED] #N category in HISTORY.md. Promote that category tier if MODERATE→consider STRONG.
  • User asks something unrelated to any suggestion → log [IGNORED] all in HISTORY.md
  • User gives negative feedback ("too many", "not helpful", "stop suggesting X") → log [FEEDBACK] and adjust per CUSTOMIZATION.md

Self-Improvement

Every 10th activation, run one learning experiment. Every 20 HISTORY.md entries, run a self-diagnostic. See SELF-IMPROVE.md for the full observe/hypothesize/experiment/validate cycle.

Key self-learning behaviors:

  • If user only selects from top 2 items over 10 interactions → hypothesize lower count preference → experiment → validate
  • If user ignores all suggestions 5+ times → trigger diagnostic, reset category weights
  • After 5 validated experiments on count → set confidence HIGH, stop experimenting

Channel Adaptation

Detect the channel and adapt format:

  • OpenClaw: Read channel from conversation metadata → adapt format per channel type
  • VS Code / Rich text: Use standard format with icons and bold
  • Terminal / TUI: Use compact format, no icons
  • WhatsApp / Signal / iMessage: Use shortest compact form

All channels share the same .nextsteps/ state. Preferences learned on one channel apply everywhere.

Security Rules (CodeGuard)

These rules are always active. Derived from cisco/software-security (Project CodeGuard):

  1. Never store secrets: .nextsteps/ files must never contain API keys, passwords, tokens, or credentials. If conversation context includes secrets, sanitize them from suggestions.
  2. Validate before writing: Before writing any .nextsteps/ file, verify the path is within .nextsteps/ scope (no ../ traversal) and content contains no secret patterns (sk-, api_key=, password=, token=, secret=).
  3. Minimize stored data: HISTORY.md stores titles and selection status only. PREFERENCES.md stores tiers and config only. BACKLOG.md stores brief descriptions only. Never store raw conversation text.
  4. Enforce file limits: PREFERENCES.md ≤ 120 lines, HISTORY.md ≤ 50 entries, BACKLOG.md ≤ 30 items. Summarize overflow, don't truncate blindly.
  5. Suggest .gitignore: On first activation, if .nextsteps/ is not in .gitignore, include it as a next step.

See SECURITY.md for the complete security protocol.

Error Recovery

  • .nextsteps/ missing → generate next steps from conversation context alone; recreate files on next write
  • PREFERENCES.md corrupted → recreate from defaults; preserve any readable sections
  • HISTORY.md overflow → summarize oldest 25 entries into PREFERENCES.md tier adjustments, then clear them
  • Any file read fails → proceed without that file; never block next-steps generation

Reliability Self-Check

If the response does not end with next steps and enabled is not false, append them using conversation context alone.

平台分布

OpenClaw

90.29%
按下载量换算1,226

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install nextsteps 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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