spex-plan

Orchestrate a research-plan-execute cycle for feature development with traceability.

2|Updated Mar 8, 2026
One-click install
npx skills add https://github.com/ran729/context-rot-skill --skill spex-plan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spex-plan
Source: https://github.com/ran729/context-rot-skill/tree/main/.claude/.claude/skills/spex-plan
Command: npx skills add https://github.com/ran729/context-rot-skill --skill spex-plan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enforces a rigorous research-plan-execute cycle for feature development, ensuring complete traceability and preventing context rot by grounding AI actions in existing code, past requirements, and established policies.

Core Features & Use Cases

  • Ambiguity Resolution: Identifies and resolves ambiguities in user requests before planning.
  • Conflict Detection: Checks for semantic conflicts with existing code, policies, and past decisions.
  • Traceable Planning: Generates detailed plans with clear requirements, decisions, and impact analyses.
  • State Management: Orchestrates a strict state machine (INIT → RESEARCH → GENERATE_PLAN → REVIEWING_PLAN → COMPILING_TASKS → EXECUTE → COMPLETE) for feature development.
  • Use Case: When a user requests a new feature, this Skill guides the AI through a structured process to understand the requirements, check for conflicts, generate a detailed plan, and then execute it, ensuring all steps are documented and traceable.

Quick Start

Use the spex plan skill to start planning a new feature.

Frequently Asked Questions about spex-plan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent context rot when using AI for feature development?▼

To prevent context rot during feature development, you can orchestrate a strict research-plan-execute cycle that grounds AI actions in existing code, past requirements, and established policies to maintain full traceability.

How do I identify semantic conflicts before planning a new feature?▼

You identify semantic conflicts by checking new feature requests against existing code, established policies, and past decisions before generating a plan, ensuring the development workflow resolves ambiguities early.

What is the best way to enforce traceability in an AI-driven development workflow?▼

The best way to enforce traceability in an AI-driven development workflow is using a strict state machine that transitions through research, plan generation, and execution while documenting all requirements and impact analyses.

How do I resolve ambiguities in software requirements before generating an execution plan?▼

You resolve ambiguities in software requirements by identifying unclear user requests during the research phase and addressing them before transitioning to the plan generation state of the development cycle.

Can I use a state machine to manage feature development from research to execution?▼

Yes, you can use a strict state machine managing states like INIT, RESEARCH, GENERATE_PLAN, REVIEWING_PLAN, COMPILING_TASKS, EXECUTE, and COMPLETE to orchestrate the entire feature development lifecycle.

What are the limitations of AI orchestration without strict state management?▼

Without strict state management, AI orchestration suffers from context rot and loses traceability, meaning AI actions may drift from established policies and fail to document impact analyses accurately.