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Agent Orchestration
Command

/sc-plan

Strategic planning and analysis for requirements and roadmaps

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From plugin
simpleclaude
11510 skills6 agents10 commands
Install
> /plugin marketplace add kylesnowschwartz/SimpleClaude

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/sc-plan

Context preview

What this command does when you run it.

Strategic planning and analysis for requirements and roadmaps

Command definition

sc-plan.md
name: sc-plan
description: Strategic planning and analysis for requirements and roadmaps
argument-hint: Planning task or requirement to analyze

sc-plan: Strategic Planning and Analysis

**Purpose**: I need to think through something - analyzes requirements, creates actionable roadmaps, and establishes clear direction for development work

Agent Orchestration and Deployment Strategy

**Efficiency First:** Handle tasks directly when possible. Use agents only when genuinely needed for:

  • Multi-step coordination requiring handoffs
  • Specialized domain expertise beyond general capability
  • Parallel work streams with interdependencies
  • Complex analysis requiring multiple perspectives
  • Operations that produce verbose intermediate output

**Direct Agent Rules (ALWAYS delegate these):**

  • **Deep codebase analysis** → Use `sc-code-explorer`
  • **Architecture design** → Use `sc-code-architect`
  • **Code quality review** → Use `sc-code-reviewer`

**Context Preservation:**

  • **Keep only**: user request, actionable recommendations, code changes, summary, next steps
  • **Discard**: intermediate outputs, full docs, verbose logs, exploratory reads

**Processing Pipeline**: Parse → Prior Work Context → Classify → Validate → Route → Execute → Synthesize

Prior Work Context (Optional)

Before planning, check if the `.agent-history/` directory exists in the project root:

  • **If present and relevant**: Read it and incorporate direct references with absolute paths into your plan. Note any conflicted areas where human judgment is needed.
  • **If it does not exist**: Proceed normally. Do not block planning on prior-work context.

Prior work context is supplementary — it improves plan quality but is never required.

Intent Recognition and Semantic Transformation

This command interprets natural language requests that express the intent: "I need to think through something" - planning, analysis, strategy, roadmap creation, or architectural decision-making.

**Command Execution:**

**Empty $ARGUMENTS**: Display usage suggestions → stop **Has content**: Parse intent → apply strategy → route execution

**Intent Processing:** Extract intent → Apply strategy matrix → Validate → Execute

**Strategy Matrix:**

| Condition | Direct Handling | Agent Required | | --------- | ------------------- | ------------------------------ | | Task Type | Simple, single-step | See "Direct Agent Rules" above | | Domain | Single, familiar | Multi-tech, unknown | | Context | Available locally | External research needed | | Output | Concise, focused | Verbose, needs filtering |

Transforms: $AGENTS into structured execution:

  • Intent: [recognized-user-intent]
  • Approach: [direct/agent with reasoning]
  • Agents: [none OR minimal-viable-set]

Output Template

## Response

[Direct answer or action taken - 1-3 sentences addressing the core request]

## Details

[Main content based on command type:
- Plan: Strategy breakdown with phases
- Work: Code changes and implementation steps
- Explore: Research findings and analysis
- Review: Issues found and quality assessment]

## Next Actions

[What to do next:
- Plan: Implementation steps to begin
- Work: Testing and validation needed
- Explore: Areas for deeper investigation
- Review: Fixes and improvements to make]

## Notes

[Optional - context, warnings, alternatives, or additional considerations]

---

**User Request**: $ARGUMENTS

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Ships withsimpleclaude

A practical minimalist AI assistant framework that transforms complex AI interactions into natural conversations. Comes with a set of user-commands, sub-agents, hooks, and utilities designed for real-world software development tasks.

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