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Secrets In Git History

Organization
UseOSINT
secrets-in-git-history

Mine GitHub, GitLab and git history for identities, infrastructure and leaked credentials using commit author emails, GitHub code search, the commit .patch endpoint, trufflehog, gitleaks, git log pickaxe and full-ref history scans. Use when investigating a developer or organisation on GitHub, finding leaked API keys, AWS keys or tokens in code, enumerating org members and their personal repos, or recovering secrets deleted from HEAD but still present in history or forks. Applies to software supply-chain risk, credential exposure response, M&A technical diligence, and insider-threat investigation. Reference at useosint.com/skills/secrets-in-git-history.

Overview

PublisherUseOSINT
RepositorySkills
Skill namesecrets-in-git-history
Stars
49
Forks
5
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Secrets In Git History 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/UseOSINT/Skills/tree/main/skills/secrets-in-git-history
  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/UseOSINT/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/skills/secrets-in-git-history .claude/skills/secrets-in-git-history
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Secrets In Git History 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 Secrets In Git History 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 Secrets In Git History 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.

Secrets in git history

A repository is three intelligence products: a list of humans with real email addresses, a map of the organisation's infrastructure, and a credential store nobody meant to publish. The costly mistake is scanning the working tree. Secrets deleted from HEAD stay in history forever, and commits deleted from a branch stay reachable through the fork network — scan only what is checked out and you are searching the one place the secret was definitely removed from.

What you're holding, and where to start

You haveStart withWhy
An organisation namePublic org members, then their personal reposCorporate repos are reviewed; personal ones are not
A developer's name or handleCommit emails across their reposYields an email nothing else gives you
A commit emailReverse-search across hosts; resolve any noreply IDLinks accounts across orgs and platforms
A suspected credential leakBoth scanners over a mirror clone, all refsHEAD-only scanning misses the point entirely
A specific string — hostname, key prefixgit log -S pickaxe across all refsTells you when it entered and when it left
A live domainCheck for an exposed .git — see the legal noteFull history from a web server, if authorized
Nothing but a company websiteCode search the org's internal domain namesFinds repos with no obvious link to the org

The commit email leak

Git records an author identity on every commit, self-asserted rather than verified. That cuts both ways: emails are trivially forged, and they are also the most reliable public link between a code-host identity and a real mailbox.

bash
git clone --mirror https://github.com/example-org/example-repo.git repo.git
git --git-dir=repo.git log --all --format='%an <%ae>' | sort -u

GitHub's web UI does not display commit emails, which leads people to assume they are hidden. They are not. Append .patch to any commit URL for a mail-format patch whose From: header carries the author name and email; the API returns the same under commit.author.email.

bash
curl -s https://github.com/example-org/example-repo/commit/<sha>.patch | head -5
curl -s https://api.github.com/repos/example-org/example-repo/commits/<sha> | jq .commit.author

The noreply pattern is the part worth understanding properly. Users with email privacy enabled commit as username@users.noreply.github.com, or in the newer form 12345678+username@users.noreply.github.com. That second form leaks the account's numeric user ID, which is immutable and survives every rename the account ever makes, so an old commit resolves to whoever holds it today:

bash
curl -s https://api.github.com/user/12345678 | jq '.login, .created_at'

That defeats a rename: an old commit under a long-abandoned handle points at the person's current identity. The complementary failure on the subject's side is enabling email privacy after years of committing — every commit before that date carries the real address, so pull the author list across all history.

Enumerating an organisation as a social graph

Corporate repositories are reviewed. Personal ones are not, and developers put the same infrastructure in both. The pivot is org to human to personal account.

Public org membership is opt-in and therefore incomplete, so triangulate: https://api.github.com/orgs/<org>/public_members gives the members who chose to be visible, while the author list across every org repository gives everyone who actually committed, including those who never went public and those who left.

From each human, pull repos, gists, followers, following, starred and watched repositories. Stars and follows are the underused ones: nobody curates them for privacy, and they map interests, employers, side projects and professional relationships more honestly than any profile field. A cluster of developers all starring the same obscure internal-looking tool are colleagues.

Public activity events cover a short recent window, but the public event stream is also published as a historical archive dataset, extending the analysis back years. Commit timestamps in aggregate give working hours and a timezone.

Code search and its real limits

Host code search finds strings across public repositories. Search what only this organisation would write: internal hostnames, private package names, cloud account IDs, the email domain. The company name finds marketing copy; the internal domain finds infrastructure.

The limits matter more than the syntax, because each is a silent gap. Code search covers the default branch only — no history, no other branches — so the deleted secret is invisible to it by construction. Not every repository is indexed: very large ones, and forks with no independent activity, are commonly excluded. Authentication is generally required and rate limits are tight. And matching is token-based, so a substring inside a longer token may not match the way you expect.

Treat code search as a discovery tool that tells you which repositories to clone. The actual work happens locally.

Everything that survives deletion

  • Secrets removed from HEAD remain in history. Deleting a file is a commit, not an erasure; rewriting history does not reach clones, forks or caches; and almost nobody rotates afterwards, because deleting felt like fixing.
  • Commits in a fork network stay reachable by SHA, even after a branch is deleted, a PR is closed unmerged, or the fork is removed. Making a repository private does not retract what reached forks, and force-pushed commits are dangling rather than gone.
  • Gists are unlisted, not private — anyone with the URL reads a "secret" gist, and URLs leak into logs, chats and tickets.
  • CI logs are public on public repositories. Masking of known secret values fails whenever a value is base64-encoded, concatenated, URL-encoded, or printed by a subprocess. Issue and PR attachments are likewise served by opaque URL and stay retrievable after the issue or repository is gone.

Scanning properly

Mirror-clone, then run both scanners — their detector sets and false-positive profiles differ and neither is a superset. Then pickaxe the strings they have no detector for: internal domains, cloud account IDs, hostnames from config files.

bash
trufflehog git file://./repo.git
gitleaks detect --source=. -v
git log --all --source -S'internal.example.com' --oneline
git log --all --diff-filter=D --name-only --format='%H %aI %an'

One warning about verification. TruffleHog can confirm a candidate is live by calling the provider's API with it. That is an interaction with a third party using someone else's credential, and it writes to their logs. Inside an authorized assessment it is normally expected. Outside one, do not.

Commands in reference/git-archaeology.md; what to grep for and how to triage a hit in reference/high-signal-indicators.md.

Exposed .git directories

A deployment that copies a working tree to a web root publishes .git with it, and from .git/config, .git/HEAD and the object store the entire history is reconstructible — source, credentials, author identities, everything.

Observing that such a path exists, through a search index or an existing scan result, is passive. Retrieving it is not. Downloading a .git directory is accessing a system and taking data from it, and no misconfiguration constitutes permission. Only inside a written authorization; otherwise, report it.

Where this goes wrong

  • Author identity is self-asserted. Anyone can commit as anyone; an email in a commit is a claim, not proof of authorship. Signed commits are the exception and the signature is what you verify, not the string.
  • Bots dominate commit counts. CI, dependency bots and merge automation produce huge volumes of commits under synthetic identities. Filter them before concluding anything about people.
  • Rebases and squash merges rewrite authorship. The committer becomes whoever performed the merge, and squashing collapses many authors into one. Absence from the log is not absence from the project.
  • Timezone inference is soft. Laptops travel, offsets get set by hand, CI commits at UTC. Corroboration only.
  • Test fixtures and documentation examples look exactly like credentials, and provider examples propagate into thousands of repos. Search first.
  • Public-by-design keys are not leaks. Publishable and referrer-locked browser keys are meant to ship; reporting one as a breach costs you credibility on the findings that are real.
  • A namesake org or typosquat is not your target, and a history-only finding is often more live than a current one, because removal without rotation is the norm.

Confidence grading

  • Confirmed — an identity link where a noreply numeric ID resolves to the account, or the same commit email appears under multiple accounts already tied to the person. A credential exposure whose format is valid, whose scope the surrounding context establishes, with a commit SHA and date.
  • Probable — a distinctive email or handle recurring across repositories with consistent commit timing, or a credential-shaped string in a real config file outside a test path.
  • Unconfirmed — a single unsigned commit's author string; a common handle; a scanner hit in a fixture or documentation example; a credential of unknown scope.
  • Rejected — a documented placeholder, a publishable key, or an expired temporary credential presented as a live exposure.

Record repository, commit SHA, date and file path for every finding. A SHA is an immutable citation; a line number is not.

Worked example

Objective: map the engineering team of a company whose site names only executives.

Code search on the internal domain svc.example-corp.net returns three repositories, none carrying the company's name. Mirror-cloning them gives fourteen distinct author emails, eleven corporate and three personal.

One author commits as 48211903+dhaverford@users.noreply.github.com. Resolving the numeric ID returns a different current username — the account was renamed, and the old handle links to conference talks and a personal blog the current one does not.

The dead end: gitleaks flags an AWS-shaped key in a test fixture, and searching the string finds it verbatim in AWS's own documentation. Discarded, and recorded as discarded so nobody re-raises it.

The real finding is a terraform.tfstate added and deleted in the same week two years ago, recovered from history. No live credential, but four internal hostnames and a cloud account ID — infrastructure for find-hidden-subdomains.

Grade: team roster probable, since commit authorship is self-asserted and uncorroborated. The rename link confirmed — the numeric ID is immutable.

Pivots

New selectorGoes to
Commit author emailswhat-an-email-reveals, what-leaked-about-you
Handles, renamed accounts, numeric IDshunt-a-handle
Real names from commits and CODEOWNERSfind-anyone
Internal hostnames from configfind-hidden-subdomains, who-owns-this-domain
Cloud account IDs, buckets, IPsfind-exposed-servers, google-like-a-spy
Committed documents and imagessecrets-in-file-metadata
Org structure and vendor stackx-ray-a-company
Deleted repos and removed pagesread-deleted-pages
Credential appearing in a public dumpfind-leaks-in-the-wild
The contributor networkgraph-the-network

Legal, disclosure, and the one hard rule

Never authenticate with a credential you find. Not to check whether it works, not read-only, not once. It is unauthorized access under computer-misuse law in most jurisdictions, and publication does not confer permission. Establish validity from format, context and metadata and nothing else. See ../../ETHICS.md.

A live-looking credential creates an obligation to disclose. Report promptly through a security contact, a security.txt route, or the host's vulnerability channel; give the repository, commit SHA and file path; state explicitly that history rewriting is not sufficient and the credential must be rotated; keep only a redacted reference; publish nothing. Hosts running automated secret-scanning partnerships may have revoked the token already, which does not remove the obligation to tell someone.

Cloning public repositories is normal use; bulk cloning and high-rate API querying are rate-limited and can breach terms. Commit emails are personal data under GDPR and equivalent regimes whatever the repository's status — collect the minimum and set a retention period.

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 Secrets In Git History AI skill do?

Mine GitHub, GitLab and git history for identities, infrastructure and leaked credentials using commit author emails, GitHub code search, the commit .patch endpoint, trufflehog, gitleaks, git log pickaxe and full-ref history scans. Use when investigating a developer or organisation on GitHub, finding leaked API keys, AWS keys or tokens in code, enumerating org members and their personal repos, or recovering secrets deleted from HEAD but still present in history or forks. Applies to software supply-chain risk, credential exposure response, M&A technical diligence, and insider-threat investig...

Why use Secrets In Git History on TypingMind?

Because you install it once and use it with any model. Secrets In Git History 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 Secrets In Git History in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UseOSINT/Skills/tree/main/skills/secrets-in-git-history. 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 Secrets In Git History?

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 Secrets In Git History?

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

Is the Secrets In Git History AI skill free?

Yes. It is published on GitHub by UseOSINT 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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