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planning-with-files-de

Create markdown files to track plans, findings, and progress across sessions.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Complex, multi-step tasks that span multiple tool calls or sessions often lose critical context and progress when the AI's volatile working memory resets, leading to repeated work, missed requirements, and forgotten decisions.

Core Features & Use Cases

  • Persistent File-Based Memory: Creates and maintains three core markdown files (task_plan.md for phase tracking, findings.md for research and technical decisions, progress.md for session logs) to store all task context permanently outside the AI's context window.
  • Automatic Session Recovery: Detects unsynced progress from previous sessions after context clears (e.g., /clear command) and provides a structured catchup report to resume work without losing state.
  • Structured Workflow Guardrails: Enforces rules like the 2-action rule (save findings after every 2 view/browser/search operations), mandatory error logging, and pre-decision plan reviews to reduce mistakes and repeated work.
  • Use Case Example: Ideal for multi-step project planning, in-depth research tasks, complex bug diagnosis, and any work requiring organization across more than 5 tool calls, such as building a new software feature from scratch or conducting a full technical architecture review.

Quick Start

Use this skill to break down your upcoming multi-step project into tracked phases, capture all research findings, and automatically restore progress if your session resets.

Frequently Asked Questions about planning-with-files-de

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

FAQPage Schema
How do I maintain progress tracking for complex multi-step tasks when AI context resets?▼

Maintain progress tracking for complex multi-step tasks by implementing a file-based planning system with markdown artifacts. This stores task context and session logs permanently outside the AI's volatile working memory, enabling automatic session recovery and preventing lost progress.

What is the best way to structure project planning files for AI-assisted research workflows?▼

The best way to structure project planning files is using three core markdown artifacts: task_plan.md for phase tracking, findings.md for technical decisions, and progress.md for session logs, ensuring persistent state tracking across multiple tool calls.

How do I recover unsynced progress after clearing an AI session for a multi-step task?▼

Recover unsynced progress after a cleared AI session using automatic session recovery hooks. The system detects previous session states and generates a structured catchup report to resume complex multi-step tasks without losing prior context or decisions.

When do I need a markdown workflow with structured guardrails for task planning?▼

You need a markdown workflow with structured guardrails when complex multi-step tasks exceed the AI's volatile working memory capacity, specifically spanning more than five tool calls or requiring persistent state tracking across multiple sessions.

Does file-based task planning work for complex bug diagnosis spanning multiple sessions?▼

File-based task planning works effectively for complex bug diagnosis by enforcing a 2-action rule to save findings after view operations, logging mandatory errors, and conducting pre-decision plan reviews to reduce repeated work across multiple sessions.

Why does the AI forget previous decisions during complex task management without persistent files?▼

The AI forgets previous decisions during complex task management because its volatile working memory resets, leading to repeated work and missed requirements. Persistent markdown files store this context permanently to prevent context loss.

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