planning-with-files

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Use this by default for non-trivial multi-step work that needs persistent planning, progress tracking, or durable notes on disk. Trigger when a task will likely span multiple tool calls, research steps, verification loops, or enough context that the plan should not live only in transient chat memory.

AI & Automation 5,144 stars 416 forks Updated today MIT

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Skill Content

# Planning with Files Work like Manus: use persistent markdown files as your working memory on disk. This is not just an optional planning style. It should be the default persistence layer for complex Codex work. Use it proactively when: - the task will likely take more than a few tool calls - progress needs to be tracked across phases - findings or evidence must be preserved while working - the task may branch, pause, or resume later - relying on chat context alone would be fragile ## Quick Start Before ANY complex task: 1. **Create `task_plan.md`** in the working directory 2. **Define phases** with checkboxes 3. **Update after each phase** - mark [x] and change status 4. **Read before deciding** - refresh goals in attention window ## The 3-File Pattern For every non-trivial task, create THREE files: | File | Purpose | When to Update | |------|---------|----------------| | `task_plan.md` | Track phases and progress | After each phase | | `notes.md` | Store findings and research | During research | | `[deliverable].md` | Final output | At completion | ## Core Workflow ``` Loop 1: Create task_plan.md with goal and phases Loop 2: Research → save to notes.md → update task_plan.md Loop 3: Read notes.md → create deliverable → update task_plan.md Loop 4: Deliver final output ``` ### The Loop in Detail **Before each major action:** ```bash Read task_plan.md # Refresh goals in attention window ``` **After each phase:** ```bash Edit task_plan.md # Mark [x], update sta...

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Author
Galaxy-Dawn
Repository
Galaxy-Dawn/claude-scholar
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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