Data Loss Guard logo

Data Loss Guard

Community
adamentwistle
data-loss-guard

Stop-and-check protocol before any destructive or hard-to-reverse operation. Use the moment a planned step would delete, overwrite, truncate, or rewrite state that cannot be trivially recreated — rm, file overwrites, git reset --hard, checkout/restore/clean of tracked files, force-push, rebasing pushed commits, branch deletion, DROP/TRUNCATE, DELETE or UPDATE without a tight WHERE, bulk API deletes, kill/pkill, terraform destroy, cache or volume wipes.

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill namedata-loss-guard
Stars
98
Forks
11
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 adamentwistle on GitHub. Read the source before you install it.

Installation

Install the Data Loss Guard 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/adamentwistle/fable-skills/tree/main/data-loss-guard
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/data-loss-guard .claude/skills/data-loss-guard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Loss Guard 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 Data Loss Guard 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 Data Loss Guard 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.

data-loss-guard

Almost every catastrophic agent failure is a destructive command run on a wrong assumption. Weak handling looks like rm -rf build/ typed from memory of what "should" be there. Strong handling predicts what the target contains, looks, and only then runs the narrowest command that does the job.

Before any destructive action:

  1. Predict, then look. Write down what you expect the target to contain (file count and names, row count, commits ahead of remote, which process owns the PID). Then check: ls -la before rm -rf; git status and git stash list before reset --hard, checkout ., restore, or clean; git log origin/branch..branch before force-push; SELECT COUNT(*) with the identical WHERE clause before DELETE or UPDATE; ps by PID before kill. If reality differs from the prediction — files you didn't create, more rows than expected, a PID you didn't start — stop and surface the difference. Don't reconcile it in your head and proceed.
  2. Distinguish recoverable from gone. Committed work is recoverable via reflog; uncommitted work is not. A dropped column or truncated table is gone. An overwritten file has no undo — including your own file-write tool on a file you did not create this session: read it first, and edit rather than replace. Rank the blast radius honestly before choosing convenience.
  3. Prefer the reversible variant: git stash over discard; move to a trash or backup path over rm; soft-delete or export-then-delete over hard delete; --dry-run wherever it exists (rsync, terraform, kubectl, many package and cloud CLIs) on the first pass, and paste its output before the real run.
  4. Scope the command tightly. Explicit paths over wildcards; absolute paths so a surprise cwd can't redirect the deletion; never rm -rf a variable-built path without first echoing the variable and checking it is non-empty and expected; kill by PID from a listing, never pkill/killall by name pattern on a shared machine; DELETE/UPDATE always with a WHERE that names specific keys, inside a transaction where the DB supports it.
  5. Destroy last. When replacing something — a file, a table, a branch, a service — the destructive step comes after the replacement is verified working, never before. A destructive command proposed as the fix for a problem you don't yet understand ("delete the cache / reset and start over") is a diagnosis shortcut: stop, find the cause, then decide whether deletion is needed at all.
  6. Uncommitted user work is sacred. If the working tree holds changes you didn't make, don't clean, checkout, stash, or overwrite them without the user's explicit say-so. Same for files, rows, or resources that exist and that you did not create.
  7. Force-push and history rewrites on shared branches need explicit user authorization, every time, and the push uses --force-with-lease.
  8. Report every destructive command. The final report lists each one you ran, what it removed, and how to recover it — or that it cannot be recovered.

Rule of thumb: cheap check, irreversible mistake — the one-command look-before-delete is always worth it.

Frequently asked questions

What does the Data Loss Guard AI skill do?

Stop-and-check protocol before any destructive or hard-to-reverse operation. Use the moment a planned step would delete, overwrite, truncate, or rewrite state that cannot be trivially recreated — rm, file overwrites, git reset --hard, checkout/restore/clean of tracked files, force-push, rebasing pushed commits, branch deletion, DROP/TRUNCATE, DELETE or UPDATE without a tight WHERE, bulk API deletes, kill/pkill, terraform destroy, cache or volume wipes.

Why use Data Loss Guard on TypingMind?

Because you install it once and use it with any model. Data Loss Guard 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 Data Loss Guard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adamentwistle/fable-skills/tree/main/data-loss-guard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Loss Guard?

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 Data Loss Guard?

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

Is the Data Loss Guard AI skill free?

Yes. It is published on GitHub by adamentwistle 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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