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planning-with-files

Create persistent task_plan.md, findings.md, and progress.md files for multi-step tasks.

561|190|Updated May 16, 2024
Search Tags:#planning#task management#workflow automation#code refactoring#persistent memory#api implementation
One-click install
npx skills add https://github.com/microsoft/semantic-link-labs --skill planning-with-files-microsoft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: planning-with-files
Source: https://github.com/microsoft/semantic-link-labs/tree/main/.claude/skills/planning-with-files
Command: npx skills add https://github.com/microsoft/semantic-link-labs --skill planning-with-files-microsoft

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust framework for managing complex, multi-step tasks by leveraging persistent file-based planning, preventing information loss and ensuring task continuity across sessions.

Core Features & Use Cases

  • File-Based Planning: Utilizes task_plan.md, findings.md, and progress.md for structured task management.
  • Session Continuity: Maintains context and goals across multiple AI interactions or interruptions.
  • Use Case: When refactoring a large codebase or implementing multiple APIs, this Skill ensures all steps, discoveries, and decisions are logged, allowing for seamless resumption of work and clear progress tracking.

Quick Start

Use the planning-with-files skill to start a new task named 'refactor-user-module'.

Frequently Asked Questions about planning-with-files

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

FAQPage Schema
How do I maintain task continuity when refactoring a large codebase across multiple sessions?▼

Persistent planning for complex tasks uses markdown files to track steps, discoveries, and decisions. This file-based approach ensures task continuity by logging progress and retaining knowledge across multiple AI interactions.

How do I track progress and findings for multi-step API implementation tasks?▼

To track progress for API implementation tasks, create `task_plan.md`, `findings.md`, and `progress.md` files in an agent cache directory. These markdown files structure task management and ensure knowledge retention.

What is file-based planning and when do I need it for workflow automation?▼

File-based planning is a system using markdown files to manage complex, multi-step tasks. It is needed for workflow automation when intricate projects require persistent memory to prevent information loss across multiple API calls or research steps.

How do I persist task context and goals across multiple AI interactions?▼

To persist task context across AI interactions, utilize a file-based planning system that writes goals and progress to markdown files. This session continuity mechanism prevents context loss and allows seamless work resumption.

Does file-based task management work without external dependencies?▼

File-based task management works without external dependencies by utilizing internal scripts and references. It relies solely on creating and updating structured markdown files within an agent cache directory for workflow automation.

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