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situated-planning-mode选址规划模式

Agent Skill

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。

总安装

2,957

周安装

122

GitHub Stars

1

下载量

966
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: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

简介

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。

SKILL.md

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

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

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

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