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Github Sensitive Data Cleanup

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daymade
github-sensitive-data-cleanup

Scans and removes sensitive data (secrets, API keys, private domains/IPs, PII) from GitHub repository history, then verifies and force-pushes safely. Use for 清理敏感数据 / 历史重写 / 泄露, "scan sensitive data", "clean git history", "remove secrets from repo", or before any force push to a public repo. Not for local-only Git recovery (use git-safety-net) or ordinary repository setup (use auto-repo-setup).

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namegithub-sensitive-data-cleanup
Stars
1.4K
Forks
222
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Github Sensitive Data Cleanup 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/daymade/claude-code-skills/tree/main/github-sensitive-data-cleanup
  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/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/github-sensitive-data-cleanup .claude/skills/github-sensitive-data-cleanup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Sensitive Data Cleanup 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 Github Sensitive Data Cleanup 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 Github Sensitive Data Cleanup 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.

GitHub Sensitive Data Cleanup

Overview

This skill guides you through safely removing sensitive data from a Git repository's history and pushing the cleaned history to GitHub. It encodes the hard-won lessons from real incidents: scan first, backup before rewriting, verify after rewriting, and never force-push to a public repo without checking its visibility and fork count.

The bundled scripts automate the mechanical parts:

  • scripts/scan_repo.py — scan the repo for secrets and private context.
  • scripts/rewrite_history.py — create a backup and rewrite history with git-filter-repo.
  • scripts/verify_cleanup.py — confirm the sensitive content is gone.
  • scripts/safe_push.py — verify repo visibility and push safely.

This skill is conservative by design. If any safety check fails, it stops and asks for human confirmation rather than continuing.

When to Use This Skill

Trigger this skill when the user:

  • Says "scan sensitive data", "扫描敏感信息", "看看仓库有没有泄露".
  • Wants to "clean git history", "sanitize history", "rewrite history", "remove secrets from history".
  • Has accidentally pushed a secret, private domain, internal IP, or PII to a public repository.
  • Is about to force-push to a public repository (even without sensitive data).
  • Mentions git filter-repo, BFG, git-filter-branch, or history rewrite.

Prerequisites

Install these tools once per machine:

bash
# git-filter-repo (modern replacement for git-filter-branch)
brew install git-filter-repo

# gitleaks (secret scanner)
brew install gitleaks

# GitHub CLI
brew install gh

The scripts assume git-filter-repo and gitleaks are on PATH. The skill will check this before running destructive operations.

Safety Rules (Non-Negotiable)

  1. Scan before you decide. Never rewrite history based on a hunch.
  2. Create a backup before rewriting. Use git bundle or a fresh bare clone.
  3. Verify repo visibility with gh repo view before any push. Do not infer public/private from the URL or directory name.
  4. Never use --no-verify to bypass hooks. If the PII Guard hook fails, fix the underlying issue or add an allowlist; do not bypass.
  5. Use --force-with-lease first. Fall back to --force only if the remote ref is stale because of the rewrite itself.
  6. Verify after rewriting. A clean git log is not enough; re-run the scanner and do an AI semantic review.
  7. Public repos with forks need extra care. Every fork keeps a copy of the old history. Coordinate with fork owners if the leaked data is high-risk.

Workflow

Step 0: Confirm the repo path and current branch

bash
cd /path/to/repo
git status --short
git remote -v

Step 1: Scan for sensitive data

Run the scanner to find what needs to be removed:

bash
uv run --with gitpython scripts/scan_repo.py --repo /path/to/repo --output /tmp/scan-report.json

The scanner auto-loads repo-specific patterns from .pii-patterns in the repo root. If that file contains real private domains, do not commit it — add it to .gitignore or keep it outside the repo. rewrite_history.py will abort if the working tree has untracked files.

To enable Layer 3 (private infrastructure context from your gitleaks config and an optional identities file):

bash
uv run --with gitpython scripts/scan_repo.py \
  --repo /path/to/repo \
  --gitleaks-config ~/scripts/git-pii-guard/gitleaks.toml \
  --identities-file ~/.config/github-sensitive-data-cleanup/identities.txt \
  --output /tmp/scan-report.json

The --gitleaks-config flag reads private-domain-context and private-ip-context rules from your private gitleaks config. The real patterns stay in your private config; nothing is copied into this public skill.

Review /tmp/scan-report.json. It includes:

  • gitleaks findings (secrets, API keys, tokens).
  • Custom pattern matches (internal IPs, phone numbers, PII).
  • Layer 3 context matches (private domains, IPs, identities from your config).
  • A reminder to do an AI semantic review for content that regex cannot catch.

If nothing sensitive is found, stop. Do not rewrite history.

Step 1.5: AI semantic review (Layer 4)

Regex scanners (Layers 1-3) cannot catch novel private context: real names, project codenames, transcript snippets, internal meeting references, or architecture descriptions. You must do an AI semantic review.

Use the prompt in references/ai_semantic_review_prompt.md on the flagged commits. Re-run the review until no new private context is found.

If you skip this step, you may push private context that gitleaks never knew to look for.

Step 2: Classify findings and choose a remediation

For each finding, decide:

  • Rotate the credential (always do this for live secrets first).
  • Remove from history (for private domains/IPs, PII, or already-rotated secrets that still reveal internal context).
  • Add to .gitignore or allowlist (for false positives only).

Live secrets must be rotated before history cleanup. Removing history does not invalidate a secret that has already been exposed.

Verify ownership claims before choosing a route. The route depends on who controls the resource: a fork/branch/document you control allows self-serve deletion; a third party's forces the GitHub Support / owner-cooperation route. Treat an audit report's annotation of who owns an account, fork, or document as an unverified claim, however confidently it is written — check gh api repos/<owner>/<repo>, gh auth status, or ask the user. (2026-09-18: an inherited report labeled an external contributor's fork as the user's own agent account, silently downgrading the route from "support ticket" to "delete it yourself" — it would have failed at execution time.)

Step 3: Prepare a replacements file

Create a text file with one replacement per line in git-filter-repo --replace-text format:

text
literal:internal.example.com==>example.com
literal:private.example.org==>example.org
literal:sk-example-aaaaaaaaaaaaaaaa==>sk-example-REDACTED

Replace these with your actual sensitive strings. Do not commit the real values; keep the replacements file outside the repository.

Use literal: for exact string matches. For regex replacements, use regex: (only if you are confident in the pattern).

Save this file outside the repo, e.g. /tmp/sensitive-replacements.txt.

Step 4: Create a backup

bash
uv run scripts/rewrite_history.py --repo /path/to/repo \
  --replacements /tmp/sensitive-replacements.txt \
  --backup /tmp/repo-backup.bundle \
  --yes

# Entity leaks live in commit MESSAGES too, not just file content. Cover both:
uv run scripts/rewrite_history.py --repo /path/to/repo \
  --replacements /tmp/sensitive-replacements.txt \
  --message-replacements /tmp/sensitive-replacements.txt \
  --backup /tmp/repo-backup.bundle \
  --yes

This script:

  1. Verifies git-filter-repo is installed and executable.
  2. Checks that the working tree is clean (no uncommitted changes or untracked files). If not, aborts.
  3. Creates a git bundle backup of the current state.
  4. Verifies the backup bundle with git bundle verify.
  5. Runs git filter-repo --replace-text. When --message-replacements is given, it also runs --replace-message so commit messages are rewritten, not just file blobs — a cleanup that only covers blobs can leave the entity naming itself in a commit message.
  6. Reports the old and new commit hashes.

If the backup or verification step fails, the script stops. Do not proceed manually.

Step 5: Verify the cleanup

bash
uv run scripts/verify_cleanup.py --repo /path/to/repo --replacements /tmp/sensitive-replacements.txt

This re-runs the scanner and also checks that none of the original sensitive strings remain in any commit. If it finds anything, go back to Step 3.

Step 6: Check visibility and push

bash
uv run scripts/safe_push.py --repo /path/to/repo --remote origin --branch main

This script:

  1. Runs gh repo view to confirm visibility, isPrivate, and forks.
  2. Warns loudly if the repo is public and has forks.
  3. Uses --force-with-lease first.
  4. Falls back to --force only if the remote ref is stale because of the local rewrite.
  5. Refuses to add --no-verify.

If the PII Guard hook fails, fix the issue and re-run. Do not bypass.

Step 7: Post-push verification

After the push succeeds:

  1. Open the repo on GitHub and confirm the sensitive strings are gone from commit history.
  2. Check that open PRs still target valid commits. Rewriting history may break existing PR branches.
  3. Notify any fork owners for high-risk leaks.

What the Bundled Scripts Do

scripts/scan_repo.py

Runs gitleaks and a custom bash/grep layer for patterns that gitleaks does not cover (private domains, internal IPs, Chinese phone numbers, certain PII). Outputs a JSON report.

bash
uv run --with gitpython scripts/scan_repo.py --repo /path/to/repo --output /tmp/report.json

scripts/rewrite_history.py

Creates a backup bundle and runs git filter-repo --replace-text. Pass --message-replacements <file> to also rewrite commit messages via --replace-message (the same replacements file usually covers both).

bash
uv run --with gitpython scripts/rewrite_history.py \
  --repo /path/to/repo \
  --replacements /tmp/sensitive-replacements.txt \
  --message-replacements /tmp/sensitive-replacements.txt \
  --backup /tmp/repo-backup.bundle \
  --yes

scripts/verify_cleanup.py

Re-runs the scanner and greps all commits for the original sensitive strings, covering both blob content (git grep over every commit) and commit messages (git log over all refs with a hash-annotated record format), so a rewrite that missed --replace-message still fails verification.

bash
uv run --with gitpython scripts/verify_cleanup.py \
  --repo /path/to/repo \
  --replacements /tmp/sensitive-replacements.txt

scripts/safe_push.py

Checks visibility and pushes safely.

bash
uv run --with gitpython scripts/safe_push.py --repo /path/to/repo --remote origin --branch main

Handling Special Cases

The repo has open PRs

Rewriting history invalidates commit refs in open PRs. After push:

  1. Ask PR authors to rebase their branches onto the new main.
  2. If the PR is yours, delete the local branch, fetch the rewritten main, and cherry-pick the changes as new commits.

The repo has forks

Public forks retain the old history until their owners sync. For high-risk leaks (live secrets, production credentials), consider:

  1. Rotating the credential immediately (mandatory).
  2. Asking GitHub Support to remove cached views of the sensitive data.
  3. Notifying fork owners with a brief, factual message.

For lower-risk leaks (internal domain names, placeholder IPs), document the rewrite and move on.

git filter-repo reports "need a fresh clone"

git-filter-repo refuses to run on repos with multiple remotes or non-origin refs. To fix:

bash
git clone --mirror /path/to/repo /tmp/repo-mirror.git
cd /tmp/repo-mirror.git
# run rewrite_history.py against the mirror

gitleaks false positives

If gitleaks flags documentation examples or test fixtures, add an allowlist entry to the repo's .gitleaks.toml or .gitleaksignore (never use --no-verify). See references/tooling_notes.md for allowlist patterns.

What This Skill Does NOT Do

  • It does not rotate live credentials for you. Rotate first, clean history second.
  • It does not remove data from GitHub's own backups or forks. It only cleans the upstream repository history.
  • It does not bypass git hooks. If a hook fails, fix the root cause.
  • It does not make secret leaks "safe." Once pushed, assume the data was seen.

References

  • references/incident-lessons.md — what went wrong in real cleanups and how this skill prevents those mistakes.
  • references/tooling_notes.md — choosing between git-filter-repo and BFG, allowlist patterns, and common errors.
  • references/ai_semantic_review_prompt.md — Layer 4 AI semantic review prompt for finding private context that regex cannot catch.

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 Github Sensitive Data Cleanup AI skill do?

Scans and removes sensitive data (secrets, API keys, private domains/IPs, PII) from GitHub repository history, then verifies and force-pushes safely. Use for 清理敏感数据 / 历史重写 / 泄露, "scan sensitive data", "clean git history", "remove secrets from repo", or before any force push to a public repo. Not for local-only Git recovery (use git-safety-net) or ordinary repository setup (use auto-repo-setup).

Why use Github Sensitive Data Cleanup on TypingMind?

Because you install it once and use it with any model. Github Sensitive Data Cleanup 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 Github Sensitive Data Cleanup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/github-sensitive-data-cleanup. 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 Github Sensitive Data Cleanup?

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 Github Sensitive Data Cleanup?

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

Is the Github Sensitive Data Cleanup AI skill free?

Yes. It is published on GitHub by daymade 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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