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File Ops

OrganizationPopular
amd
file-ops

Read, write, and edit files on disk safely — locate the right file, change only what was asked, and confirm before anything destructive. Use when the user wants a file read, created, modified, or searched for by name or content.

Overview

Publisheramd
Repositorygaia
Skill namefile-ops
Stars
1.6K
Forks
176
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by amd on GitHub. Read the source before you install it.

Installation

Install the File Ops 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/amd/gaia/tree/main/hub/skills/file-ops
  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/amd/gaia.git /tmp/gaia
mkdir -p .claude/skills
cp -r /tmp/gaia/hub/skills/file-ops .claude/skills/file-ops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable File Ops 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 Ops 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 Ops 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.

File Ops

Touching a file on someone's disk is not like answering a question — a wrong guess doesn't just look bad, it overwrites something they cannot get back. Slow down in proportion to how hard the change is to undo.

Procedure

  1. Find it before you touch it. Don't guess a path from memory or the user's description. Use find_files(query) for "the report from last week" style requests, or search_file_content(pattern) when you know a string that must be inside the file but not which file it's in. Confirm the exact path with get_file_info before acting on it — a near-miss filename is easy to write to by accident.
  2. Read before you write or edit. read_file(file_path) first, every time — even for a file you edited two turns ago. It may have changed since, and edit_file only succeeds against the file's current exact content.
  3. Prefer edit_file over write_file for existing files. edit_file replaces one exact string (old_content → new_content); it only changes what you targeted. write_file replaces the entire file — reach for it only when creating a new file or when you genuinely mean to replace everything.
  4. When edit_file fails with "content not found," re-read the file. Do not retry with a guessed variation of the string. The failure means your copy of the file is stale or the whitespace doesn't match byte-for-byte — rewriting from memory to force it through is how you silently discard whatever changed since you read it.
  5. Confirm before anything destructive. Overwriting a file whose existing content is unrelated to the new content, replacing most of a file in one edit_file call, or writing to a path that already holds something important — stop and ask with request_user_input first. A backup gets created automatically on overwrite/edit, but "recoverable with effort" is not the same as "the user expected this."
  6. Report what actually changed. State the path, and for edits, name what changed rather than pasting the whole diff back at the user unless they ask for it.

Guardrails are not bugs

write_file and edit_file refuse system directories, credential-bearing dotfiles such as environment files and SSH key directories, and anything above a 10 MB size limit — these are the platform's sandbox, not something to route around. If a write is denied, that is the answer; report it and ask the user where they'd actually like the file, don't look for another tool that skips the check.

What this skill does not cover

read_file is for text — Python, Markdown, config, plain text. It refuses binary document formats (PDF, DOCX, XLSX, and similar) outright, because reading them raw produces garbage the model then reasons over as if it were real content. Those need index_document + query_documents instead — see the document-brief skill.

Fork this

Pin step 1 to a project's actual file layout (config always lives at X, generated output always goes to Y) and the search step collapses to a direct path, turning this into a fast in-place editor for one codebase.

Frequently asked questions

What does the File Ops AI skill do?

Read, write, and edit files on disk safely — locate the right file, change only what was asked, and confirm before anything destructive. Use when the user wants a file read, created, modified, or searched for by name or content.

Why use File Ops on TypingMind?

Because you install it once and use it with any model. File Ops 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 Ops in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/amd/gaia/tree/main/hub/skills/file-ops. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use File Ops?

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 Ops?

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

Is the File Ops AI skill free?

Yes. It is published on GitHub by amd under the MIT 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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