situated planning mode

v1.0.0

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, cons...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for phallusophy/situated-planning-mode.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "situated planning mode" (phallusophy/situated-planning-mode) from ClawHub.
Skill page: https://clawhub.ai/phallusophy/situated-planning-mode
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install situated-planning-mode

ClawHub CLI

Package manager switcher

npx clawhub@latest install situated-planning-mode
Security Scan
Capability signals
CryptoCan make purchases
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
VirusTotalVirusTotal
Benign
View report →
OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
The name/description (planning assistant) match the SKILL.md: it guides users through staged planning questions, generates options+descriptions, and produces a plan. It does not request unrelated binaries, environment variables, or config paths.
Instruction Scope
Instructions are focused on planning and dynamic question generation. The skill explicitly instructs the agent to autonomously launch subagent research (sessions_spawn) when knowledge is insufficient and to call memory_search/memory_get for prior context. This is coherent with the stated purpose, but it gives the agent discretion to spawn research subagents and consult memory — review what those platform primitives can access in your environment.
Install Mechanism
No install spec and no code files are present (instruction-only), so nothing is written to disk or downloaded during install.
Credentials
The skill requests no environment variables, credentials, or config paths. The use of memory_search/memory_get and sessions_spawn is declared in the instructions and is proportionate to a planning/ research workflow.
Persistence & Privilege
always is false and the skill does not request persistent system-wide changes. It does permit autonomous subagent invocation per its instructions, which is consistent with platform defaults and the skill's research behavior.
Assessment
This skill is internally coherent and contains no hidden installs or credential requests. Before installing, confirm what your platform's sessions_spawn and memory APIs are permitted to access (internet, external APIs, and stored memory) because the skill explicitly instructs the agent to spawn research subagents and consult memory when it lacks knowledge. If you do not want the agent to perform network research or access stored memories automatically, restrict the skill's permissions or disallow autonomous subagent invocation. Otherwise, the skill appears safe for its stated planning purpose.

Like a lobster shell, security has layers — review code before you run it.

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93downloads
1stars
1versions
Updated 3w ago
v1.0.0
MIT-0

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

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