File Organizer logo

File Organizer

Organization
Amazon-Quick
file-organizer

Scan a folder, analyze file contents and naming patterns, then propose renaming, categorization, and folder structure improvements. All changes require explicit user confirmation before execution. Use when asked to 'organize this folder', 'clean up my files', 'sort these documents', 'rename my files', 'folder structure suggestions', 'tidy up this directory', or any request to restructure a messy directory.

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill namefile-organizer
Stars
50
Forks
4
Bundled files
1
LicenseMIT-0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the File Organizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/general-productivity/file-organizer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/general-productivity/file-organizer .claude/skills/file-organizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable File Organizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads File Organizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so File Organizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

Analyzes the contents of a specified folder, examining file names, types, dates, and optionally content, then proposes an organized folder structure with consistent naming. Presents a full reorganization plan for user approval before touching any files. Never moves, renames, or deletes files without explicit confirmation.

Workflow

<Definition - Organization Strategy> How to group files into folders:

  • date: Group by creation/modification date (year/month or year/quarter)
  • type: Group by file extension category (documents, images, spreadsheets, code, media, archives)
  • project: Group by inferred project association (from naming patterns or content)
  • topic: Group by content topic (requires reading file headers/content)
  • auto-detect: Analyze existing patterns and pick the most natural grouping </Definition - Organization Strategy>

<Definition - Naming Conventions>

  • kebab-case: lowercase-words-separated-by-hyphens.ext
  • snake_case: lowercase_words_separated_by_underscores.ext
  • PascalCase: EachWordCapitalized.ext
  • camelCase: firstWordLowerThenCapitalized.ext
  • date-prefix: YYYY-MM-DD_descriptive-name.ext
  • original: keep existing filenames, only reorganize into folders
  • other: user-defined custom pattern (collected during preferences step) </Definition - Naming Conventions>

<Definition - Reorganization Plan> A table mapping every file from its current location to its proposed new location, including the action type (move, rename, move+rename, unchanged). Must include a summary count and a rollback reference. </Definition - Reorganization Plan>

<Workflow - Setup Preferences description="Collect and save the user's file organization preferences to memory." tools=[recall_memories, save_to_memory] triggers=["set up file organization preferences", "change my file organization settings", "update my organization preferences"]

  1. [Agent] Recall existing file organization preferences from memory. Query: "file organization preferences, max folder depth, collision strategy, scan threshold". [Decide] Are existing preferences found?

    • Yes -> [Ask user] "Here are your current preferences:
      • Max folder depth: [value]
      • Collision strategy: [value]
      • Large folder scan threshold: [value]
      • Default naming convention: [value] Want to update any of these?"
    • None found -> proceed to step 2.
  2. [Ask user] Collect preferences:

    • "Max folder depth? (how many levels of nesting, e.g. 3)"
    • "Collision strategy? When two files would get the same name: numeric suffix (_01, _02), parenthetical suffix ((1), (2)), or ask each time?"
    • "Large folder threshold? Above how many files should I sample for content analysis instead of reading all? (e.g. 200)"
    • "Default naming convention? (kebab-case, snake_case, PascalCase, camelCase, date-prefix, original, or describe your own)" Validate: User provides at least max depth and collision strategy. If fails: Use sensible defaults (depth: 3, collision: numeric suffix, threshold: 200, naming: kebab-case) and confirm with user.
  3. [Agent] Save all preferences to memory with hint: "file organization preferences for file-organizer skill". Validate: save_to_memory confirms signal recorded. If fails: Report the error and present preferences in chat so user has a record.

</Workflow - Setup Preferences>

<Workflow - Organize description="Scan a folder, analyze content, propose and execute a reorganization plan using saved preferences." tools=[get_current_time, recall_memories, save_to_memory, folder_list, file_read, file_read_pdf, file_read_docx, file_read_pptx, file_move, file_copy, file_write, folder_create, open_in_session_tab, run_python] triggers=["organize this folder", "clean up my files", "sort these documents", "rename my files", "folder structure suggestions", "tidy up this directory"]

  1. [Agent] Recall the user's file organization preferences from memory. [Decide] Are preferences found?

    • Yes -> apply as defaults for this run.
    • No -> [Ask user] "I don't have your organization preferences saved. Would you like to set them up now (recommended for future runs) or just tell me what you want for this one-time run?"
      • Set up -> execute <Workflow - Setup Preferences>, then return here.
      • One-time -> collect minimal preferences inline (depth, collision, naming) and proceed without saving.
  2. [Agent] Validate {{folder_path}} exists and is within allowed folders using folder_list. Validate: Folder exists, contains at least one file, access is permitted. If fails: [Ask user] "That folder doesn't exist or isn't in your allowed folders. Please provide a valid path."

  3. [Agent] Scan folder contents with folder_list. For each file, capture: name, extension, size, last modified date. [Decide] File count > 500?

    • Yes -> use run_python with os.listdir for full listing. [Decide] File count > user's scan threshold preference?
    • Yes -> sample files for content analysis in step 4. Note the sampling in output.
    • No -> analyze all files in step 4. Validate: Complete file inventory captured. If fails: Report the error and ask user if they want to proceed with a partial listing.
  4. [Think] Determine organization strategy. [Decide] Is {{organization_strategy}} set to "auto-detect"?

    • Yes -> Analyze file type distribution, naming patterns, content themes (read first 500 chars of text-based files), date clustering, existing partial organization. Select the most natural grouping axis.
    • No -> use the user's specified strategy directly. [Decide] Is {{naming_convention}} set to "other"?
    • Yes -> [Ask user] "Describe your custom naming pattern (e.g., 'YYYY-MM-DD_project_description')."
    • No -> proceed with the selected convention. Validate: A clear strategy and naming convention are determined. If fails: Default to organizing by file type with user's preferred naming convention.
  5. [Agent] Generate the reorganization plan using run_python. For every file, compute:

    • Target folder (based on strategy)
    • Target filename (based on naming convention, preserving extension)
    • Action type: move, rename, move+rename, or unchanged
    • Collision check: resolve using user's collision preference. Format as a markdown table: | # | Current Path | Target Path | Action | Notes | Include summary: files to move, rename, new folders to create, unchanged, collisions resolved, skipped items. Validate: Every file accounted for. No unresolved collisions. No target paths outside allowed folders. If fails: Regenerate with a simpler structure.
  6. [Ask user] Present the full reorganization plan with summary. Ask: "Do you want me to: A) Execute the full plan as shown B) Execute with modifications (tell me what to change) C) Only rename files (no moves) D) Only reorganize into folders (no renames) E) Cancel and save the plan as a reference only" Validate: User explicitly approves with A, B, C, or D. If fails (user picks E or says no): Save plan to artifacts/file-organization-plan-[date].md, open in session tab, stop.

  7. [Agent] Execute the approved plan:

    1. Create new folders with folder_create.
    2. Move/rename files with file_move (one at a time).
    3. Log each result. [Decide] Did any operation fail?
    • Yes -> Stop. Report what completed and what remains. [Ask user] "Continue with remaining files or stop here?"
    • No -> proceed to step 8. Validate: All approved operations completed successfully.
  8. [Agent] Generate summary report, save to artifacts/file-organization-report-[date].md. Include:

    • Results count (moved, renamed, folders created, errors)
    • Final folder structure (tree view)
    • Full changes log
    • Rollback instructions (reverse mapping) Open report in session tab. [Decide] Did the user override any preferences during this run?
    • Yes -> [Agent] Save updated preferences to memory. Validate: Report is accurate and rollback reference is complete. If fails: Output summary directly in chat.

</Workflow - Organize>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the File Organizer AI skill do?

Scan a folder, analyze file contents and naming patterns, then propose renaming, categorization, and folder structure improvements. All changes require explicit user confirmation before execution. Use when asked to 'organize this folder', 'clean up my files', 'sort these documents', 'rename my files', 'folder structure suggestions', 'tidy up this directory', or any request to restructure a messy directory.

Why use File Organizer on TypingMind?

Because you install it once and use it with any model. File Organizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install File Organizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/general-productivity/file-organizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use File Organizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with File Organizer?

As many as you like. As long as a model supports skills, you can use File Organizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the File Organizer AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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