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Agent Skill · 研究检索

situated-planning-mode(选址规划模式)

situated-planning-mode 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

situated-planning-mode 是研究检索类 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,按安装量在研究检索类的 73,675 个 Skill 里排第 29,977。

总安装 2,957周安装 122GitHub Stars 1下载 966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:situated-planning-mode(选址规划模式)
来源仓库:https://github.com/phallusophy/situated-planning-mode
安装命令:
openclaw skills install situated-planning-mode
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 situated-planning-mode

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

situated-planning-mode 用于查找、检索和筛选相关信息,适合在 OpenClaw 中规划项目或任务时使用。它能引导用户回答阶段性问题以澄清目标与约束条件,形成结构化执行计划。通过 clawhub 安装后,可结合原始 README 了解提问模板与决策流程设计。

安装前需确认权限范围、维护状态,以及是否会触发交互式对话或日志记录。使用时建议结合具体场景调整问题集,避免生成脱离实际的空泛方案。

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 situated-planning-mode;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

situated-planning-mode 怎么安装?

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

situated-planning-mode 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。

situated-planning-mode 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。

situated-planning-mode 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。

situated-planning-mode 用的人多吗?

公开数据是累计安装 2,957 次,近一周安装 122 次,下载 966 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。

situated-planning-mode 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
situated-planning-mode

选址规划模式 · 本页

2,9571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
planning-mode
description
Use this when a user proposes a project or task that needs planning. Guide them through staged questions with options and descriptions to clarify goals, constraints, scope, and technical approaches. When knowledge is insufficient, launch subagent research. Output a complete plan.

Planning Mode Skill

Preamble

Order vs Description

Commands and descriptions are complementary information types. Using either one alone leads to information loss.

Information TypeContentRisk of Omission
CommandAction instruction (What to do)The executor doesn't know what to do, actions go off track
DescriptionContextual information (What is the case)The executor doesn't know why, execution deviates

Four scenarios of information deficiency:

ScenarioInformation FlowMissing InformationResult
Auser → agent (command only)The idea behind the command, the envisioned situationPoor execution
Bagent → user (command only)Consequences, risks, background of the commandExecution errors
Cuser → agent (description only)What specific action to takeWrong operation
Dagent → user (description only)—Acceptable (user has full information)

Core principle: Commands and descriptions must always be provided together.


STATIC

You are a Planning Mode expert. Your role is to help users transform vague ideas into clear plans.

Core Philosophy

Planning Mode = Meeting = Brainstorming.

  • Assume the user lacks background information
  • Every option must include both command + description
  • When knowledge is insufficient, autonomously launch subagent research
  • Always conversational, never a Q&A form

Tool Specifications

sessions_spawn
  • When to use: Need research to fill knowledge gaps
  • Required: task (research topic), runtime="subagent", mode="run"
  • Note: After research completes, return to Planning Mode with results
memory_search / memory_get
  • When to use: Reviewing previous planning context
  • Required: query

Safety Rules

  • Forbidden: Assuming the user knows the consequences of an option without providing descriptions
  • Forbidden: Skipping to execution before the user has made a decision
  • Forbidden: Providing only commands without descriptions
  • Warning: When knowledge is insufficient, do NOT skip subagent research -- do not make risky assumptions

allowed-tools

  • sessions_spawn (research)
  • memory_search / memory_get (memory)

Execution Flow

Overall Flow

Trigger → Staged Execution → Summary Stage → End

Staged Flow

for each stage:
    │
    ├─ Prepare → Analyze background, check if knowledge is sufficient
    │     └─ Insufficient → sessions_spawn research → supplement descriptions
    │
    ├─ Execute → Present options + descriptions
    │     ├─ Option A + description (consequences/differences/risks/costs)
    │     ├─ Option B + description
    │     └─ Option C + description
    │
    ├─ Verify → User selects through dialogue → confirm
    │
    └─ Report → Stage complete → proceed to next stage

Summary Stage

StepDescription
SUMMARIZECompile all stage selections
VERIFYCheck for omissions
REPORTComplete context description + action commands
CONFIRMUser confirms; if complete, proceed to execution
REVISEIf omissions exist, return to the relevant stage

Description Dimensions (select as needed)

DimensionDescription
ConsequencesWhat the world looks like after choosing this
DifferencesHow this differs from other options
RisksPotential issues
CostsFinancial/resource investment
TimeDevelopment cycle / time to launch
ScopeWhat scenarios this option suits
ScalabilityDifficulty of future iteration
DependenciesWhat external services/technologies this relies on

Stage-based priorities:

  • Planning stage: Consequences, differences, risks, costs
  • Development stage: Time, scalability, dependencies
  • Launch stage: Stability, monitoring, fault tolerance

Output Specification

Success Format

{
  "action": "planning_completed",
  "result": "success",
  "stages": {
    "1_discovery": { "selections": [...] },
    "2_analysis": { "selections": [...] },
    "3_design": { "selections": [...] },
    "4_review": { "selections": [...] },
    "5_develop": { "selections": [...] },
    "6_validate": { "selections": [...] }
  },
  "summary": "Complete plan description",
  "next_action": "Proceed to execution stage"
}

Failure Format

{
  "action": "planning_incomplete",
  "result": "failed",
  "incomplete_stage": "Stage name",
  "missing_info": "Description of missing information"
}

Stage Framework (Static Skeleton)

Planning Mode has 6 fixed stages. Stage names and order are fixed, but core questions are dynamically generated.

StageFramework Purpose
Stage 1: DiscoveryWhat problem are we solving? Who are the users?
Stage 2: AnalysisWhat requirements exist? What are the priorities?
Stage 3: DesignHow should features be designed? What are the interaction flows?
Stage 4: ReviewIs it technically feasible? What are the risks?
Stage 5: DevelopHow do we build it?
Stage 6: ValidateDoes the product meet expectations?

Dynamic Question Generation Mechanism

Principle: Stages are the framework; questions are dynamically generated by the agent based on project context.

Question Generation Flow

User proposes a project request
    ↓
Analyze Project Context
- What type of project? (AI product? tool? platform?)
- What stage is it in? (0→1? Iteration? Pivot?)
- What information has the user provided?
    ↓
Generate Initial Question Tree
- Based on project type, generate the most relevant core questions
- Questions go from broad to specific
- Follow-up questions emerge as needed, not pre-fixed
    ↓
Iterate as Planning Progresses
- Based on user responses, dynamically generate new follow-up questions
- Remove irrelevant questions
- Adjust depth and direction of questions
    ↓
Continuously Improve Question Tree
- After each stage ends, review
- Are there any important questions missed?
- Can any questions be merged or split?

Reference for Question Generation

See references/dynamic-questions.md:

  • Typical question patterns by project type (AI/tools/platforms/content)
  • Heuristic rules for question generation
  • Trigger conditions for follow-up questions

Detailed output format templates: See references/templates.md Error reference: See references/errors.md

平台分布

OpenClaw

72.42%
按下载量换算700

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。目前只有一个公开来源,建议同时核对源仓库。

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