skills.rest
Herxinsasa
HerxinsasaCOMMUNITY
Explore all skills by this author →
GitHub

planning-with-files-ar

Track multi-step task progress using markdown working memory files.

1|Updated Apr 22, 2026
Search Tags:#workflow automation#task planning#project tracking#session recovery#multi-step tasks#context persistence#markdown workflow
One-click install
npx skills add https://github.com/Herxinsasa/Skills-Collector --skill planning-with-files-ar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: planning-with-files-ar
Source: https://github.com/Herxinsasa/Skills-Collector/tree/main/planning-with-files/skills/planning-with-files-ar
Command: npx skills add https://github.com/Herxinsasa/Skills-Collector --skill planning-with-files-ar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill solves the critical issue of losing context, progress, and key decisions during long, multi-step complex tasks when working with AI assistants, which have limited context windows and often require session resets or interruptions that erase critical work state.

Core Features & Use Cases

  • Persistent External Working Memory: Uses three dedicated markdown files (task_plan.md, findings.md, progress.md) stored in your project directory to act as unlimited, persistent memory that survives context resets and session interruptions.
  • Automatic Session Recovery: Includes a cross-platform session catchup script that automatically restores context from previous sessions after a /clear command, including unsynced work and recent code changes.
  • Structured Guided Workflow: Enforces a standardized 5-stage task process (requirements, planning, execution, testing, delivery) with built-in error logging, triple failure protocol, and progress tracking rules to reduce errors and rework.
  • Use Cases: Ideal for product analysis, requirement review, technical selection, complex bug diagnosis, end-to-end development workflows, and any task spanning multiple tool calls or work sessions.

Quick Start

Ask your AI assistant to use the planning-with-files-ar skill to organize your complex multi-step project, create the required tracking files, and monitor progress across work sessions.

Frequently Asked Questions about planning-with-files-ar

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

FAQPage Schema
How do I maintain context for multi-step tasks when an AI session resets?▼

To maintain context for multi-step tasks after an AI session resets, you can use persistent markdown-based working memory. This approach stores task plans, findings, and progress in dedicated files that survive context resets and enable automatic session recovery.

What is the best way to track complex project workflows across interrupted AI sessions?▼

The best way to track complex project workflows across interrupted AI sessions is by using external markdown files as persistent memory. This method enforces a structured multi-stage task process and logs progress automatically to ensure seamless work resumption.

How do I prevent progress tracking failure during long technical troubleshooting?▼

To prevent progress tracking failure during long technical troubleshooting, implement a structured 5-stage task workflow with built-in error logging and persistent markdown files. This captures key decisions and unsynced work, ensuring you never lose critical state.

Can I use markdown files to recover unsynced code changes after a context window limit?▼

Yes, you can use markdown files to recover unsynced code changes after hitting a context window limit. A cross-platform session catchup script automatically restores context from previous sessions, including recent code changes and prior task progress.

Does structured task planning reduce errors in end-to-end development workflows?▼

Structured task planning reduces errors in end-to-end development workflows by enforcing a standardized 5-stage process. This includes requirements, planning, execution, testing, and delivery phases with built-in error logging protocols to minimize rework.

What are the limitations of relying on AI context windows for complex task planning?▼

The limitation of relying on AI context windows for complex task planning is the critical loss of progress, context, and key decisions during interruptions. Bypass this by using persistent external markdown working memory for unlimited state retention.

Related Skills

Explore More Product & Management Skills→
bahayonghangbahayonghang
16

planning-with-files

Initialize and update Markdown planning files for multi-phase tasks.

Community
Intermediate
workinprogress-aiworkinprogress-ai

devenv-plan-status

Analyzes implementation plan markdown files or GitHub issues to compute task completion metrics.

Official
Intermediate
Clay-HHKClay-HHK
2

planning-with-files

Track multi-step task progress using persistent markdown files.

Community
Advanced
barkleesandersbarkleesanders
5

file-todos

Create and track file-based todos for code review feedback and technical debt.

Community
Intermediate