How does this skill help with context window limits?

It encourages storing large research findings and intermediate data in files rather than stuffing the chat context, keeping the AI focused on the immediate execution task.

When should I use the planning-with-files skill?

This skill is best used for any task involving three or more steps, deep research, or building complex features that require tracking specific decisions and progress.

What is the benefit of using files for planning in Claude Code?

Using files like task_plan.md provides Claude with a persistent 'memory on disk' that survives across tool calls and prevents critical goal context from being lost during long sessions.

What are the core files created by this pattern?

The pattern typically initializes a task_plan.md for progress tracking, a notes.md for research findings, and a final deliverable markdown file.

Persistent Task Planning & Memory

Name: Persistent Task Planning & Memory
Author: pyinx

bypyinx

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生産性とワークフロー

Implements a file-based workflow for structured planning, progress tracking, and persistent knowledge storage during complex tasks.

概要

This skill transforms how Claude handles complex workflows by utilizing persistent markdown files as external working memory on disk. By establishing a structured three-file pattern—task plans, research notes, and deliverables—it ensures that project goals remain clear, progress is tracked accurately across multiple tool calls, and large amounts of data are stored efficiently without overwhelming the active context window. It is an essential tool for developers and researchers managing multi-step projects that require rigorous organization, decision logging, and error recovery.

主な機能

Persistent working memory to track multi-step project progress
Context optimization by storing large findings in external files
2 GitHub stars
Structured 'Read Before Decide' workflow for improved accuracy
Automated 3-file pattern for planning, research, and delivery
Standardized task_plan.md template with phase tracking and error logging

ユースケース

Conducting deep technical research and synthesizing findings into documentation
Executing complex, multi-phase software development lifecycles
Managing large-scale code refactoring or multi-file migrations

概要

主な機能

Persistent working memory to track multi-step project progress
Context optimization by storing large findings in external files
2 GitHub stars
Structured 'Read Before Decide' workflow for improved accuracy
Automated 3-file pattern for planning, research, and delivery
Standardized task_plan.md template with phase tracking and error logging

ユースケース

Conducting deep technical research and synthesizing findings into documentation
Executing complex, multi-phase software development lifecycles
Managing large-scale code refactoring or multi-file migrations