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Github Secret Hunting

CommunityPopular
uphiago
github-secret-hunting

Find leaked API keys, tokens, and credentials in public GitHub repositories.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namegithub-secret-hunting
Stars
1.3K
Forks
213
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 uphiago on GitHub. Read the source before you install it.

Installation

Install the Github Secret Hunting 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.
  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/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/recon/github-secret-hunting .claude/skills/github-secret-hunting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Secret Hunting 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 Secret Hunting 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 Secret Hunting 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 Secret Hunting

Scan public GitHub repositories for leaked API keys, tokens, passwords, and internal infrastructure details. Developers accidentally push secrets constantly — this skill uses targeted dorking, automated scanning tools, and real-time monitoring to find credentials before the developer notices and revokes them.

When to Use

  • Target has public repositories under an organization account.
  • JS bundle analysis reveals internal service names — search GitHub for related config files.
  • Need to find valid API keys for cloud services, payment gateways, or third-party integrations.
  • The target uses CI/CD systems that may leak tokens in build logs or workflow files.
  • Want real-time monitoring for new secret leaks from the target org.

Prerequisites

  • terminal with python3, curl, git.
  • GitHub Personal Access Token (only public_repo scope needed).
  • Tool dependencies: TruffleHog, GitDorker, gitleaks.

Quick Detection

bash
# Basic GitHub code search for sensitive patterns in target repos
echo "target.com" | while read domain; do
  curl --max-time 30 --connect-timeout 10 -s -H "Authorization: token $GITHUB_TOKEN" \
    "https://api.github.com/search/code?q=$domain+filename:.env" \
    | jq '.items[]?.html_url'
done

Procedure

Phase 1 — Targeted Dorking with GitDorker

bash
# Clone the dork collection and run against target
git clone https://github.com/Proviesec/github-dorks
python3 GitDorker.py \
  -tf $GITHUB_TOKEN \
  -q target.com \
  -d dorks/medium_dorks.txt \
  -o gitdorker_target.txt

# Also search by employee emails found in LinkedIn or metadata
python3 GitDorker.py \
  -tf $GITHUB_TOKEN \
  -q "john.doe@target.com" \
  -d dorks/medium_dorks.txt

# Custom dork: find env files
python3 GitDorker.py -tf $GITHUB_TOKEN \
  -q "org:target filename:.env DB_PASSWORD" -d dorks/medium_dorks.txt

Phase 2 — TruffleHog Deep Scanning

bash
# Scan a specific repo (finds secrets even in deleted commits)
trufflehog git https://github.com/target/repo --results=verified

# Scan entire GitHub org
trufflehog github --org=target --token=$GITHUB_TOKEN \
  --only-verified --threads=20 --json > trufflehog_org.json

# Docker variant
docker run --rm -it trufflesecurity/trufflehog:latest \
  github --only-verified --org=target

# Parse verified secrets
cat trufflehog_org.json | jq -r 'select(.Verified == true) | "\(.DetectorName): \(.RawV2)"'

Phase 3 — Real-Time Monitoring with shhgit

bash
# Monitor globally for secrets being pushed right now
shhgit --search-query \
  'path:*.env OR "DB_PASSWORD=" OR "AWS_ACCESS_KEY_ID=" OR "-----BEGIN RSA PRIVATE KEY-----"'

# Monitor specific org
shhgit --search-query \
  'target.com (path:*.env OR "DB_PASSWORD=" OR "api_key=")'

Phase 4 — File Type and Extension Search

bash
# git-wild-hunt: find specific file types
python3 git-wild-hunt.py \
  -s "org:Target extension:json filename:creds language:JSON"
python3 git-wild-hunt.py \
  -s "org:Target extension:sql filename:backup"
python3 git-wild-hunt.py \
  -s "target.com gitlab_token"

# Manual search patterns via GitHub API
for pattern in "filename:.env DB_PASSWORD" "filename:credentials.json" \
               "filename:config.json api_key" "filename:id_rsa" \
               "filename:.npmrc" "extension:pem BEGIN RSA" \
               "filename:service-account.json"; do
  curl --max-time 30 --connect-timeout 10 -s -H "Authorization: token $GITHUB_TOKEN" \
    "https://api.github.com/search/code?q=target.com+$pattern" \
    | jq '.total_count, (.items[:3][].html_url)'
done

Phase 5 — Hardcoded Credential Verification

bash
# Pipeline: find → extract → verify
echo "target.com" | gau | grep -E '\.js$|\.json$|\.env$|\.config$' \
  | httpx -silent -mc 200 \
  | parallel -j 10 "curl --max-time 30 --connect-timeout 10 -s {} | grep -Eo \
    '(?:api[_-]?key|secret|token)[\"'\''']?\s*[:=]\s*[\"'\''']?([A-Za-z0-9_\-]{20,})' \
    | tee -a api_keys.txt"

# Verify found keys
for key in $(cat api_keys.txt | awk -F':' '{print $2}' | tr -d '"'\'' ' | sort -u); do
  # OpenAI
  curl --max-time 30 --connect-timeout 10 -s "https://api.openai.com/v1/models" -H "Authorization: Bearer $key" | jq '.data[].id' 2>/dev/null && echo "VALID OPENAI: $key"
  # GitHub
  curl --max-time 30 --connect-timeout 10 -s "https://api.github.com/user" -H "Authorization: token $key" | jq '.login' 2>/dev/null && echo "VALID GITHUB: $key"
done

Phase 6 — GitLab Private Instances

bash
# Discover self-hosted GitLab
# Check: gitlab.target.com, git.target.com, code.target.com
curl --max-time 30 --connect-timeout 10 -sk "https://gitlab.target.com/api/v4/projects?visibility=public"

# With a found token
curl --max-time 30 --connect-timeout 10 --header "PRIVATE-TOKEN: $GITLAB_TOKEN" \
  "https://gitlab.target.com/api/v4/user"
curl --max-time 30 --connect-timeout 10 --header "PRIVATE-TOKEN: $GITLAB_TOKEN" \
  "https://gitlab.target.com/api/v4/projects?membership=true&simple=true"

# Deep scan for secrets across accessible repos
gitleaks detect \
  --source https://gitlab.target.com \
  --access-token $GITLAB_TOKEN -v

Phase 7 — Metadata Extraction from Public Documents

bash
# metafinder: downloads public documents and extracts metadata
# Reveals usernames, software versions, internal file paths, email patterns
metafinder -d "target.com" -l 10 -go -bi -ba -o metadata_target.txt
metafinder -d "dev.target.com" -l 10 -go -bi -ba -o metadata_dev.txt

# Manual: check PDF metadata
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/document.pdf" -o doc.pdf
exiftool doc.pdf | grep -i "author\|creator\|producer"

Pitfalls

  • Most search results are documentation and examples, not real leaks. Focus on .env, .config, .npmrc, and CI/CD workflow files.
  • Rate limiting on GitHub API is strict. Use multiple tokens or rotate IPs.
  • Verified secrets may already be revoked. Always verify before reporting.
  • Self-hosted GitLab instances may block external scanning. Test connectivity first.
  • Never use found credentials for unauthorized access. Verify minimally, document, and report.

Verification

  1. TruffleHog or GitDorker identifies a potential secret with context.
  2. Verify the secret by making a minimal API call (e.g., GET /user for GitHub tokens).
  3. Confirm the secret was committed recently (check commit date) — stale secrets are lower priority.
  4. Check if the repo is public and the secret grants meaningful access (admin vs read-only).
  5. Document the exact file path, commit hash, and line number for the report.

Related Skills

  • js-secrets-extraction — Find API keys and endpoints in JavaScript bundles that may lead to GitHub repos.
  • hardcoded-credential-hunt — Detect hardcoded passwords in HTML, JS, and API responses.
  • source-leak-hunt — Find exposed config files (.env, .git) on live web servers.

Frequently asked questions

What does the Github Secret Hunting AI skill do?

Find leaked API keys, tokens, and credentials in public GitHub repositories.

Why use Github Secret Hunting on TypingMind?

Because you install it once and use it with any model. Github Secret Hunting 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 Secret Hunting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/recon/github-secret-hunting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Github Secret Hunting?

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 Secret Hunting?

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

Is the Github Secret Hunting AI skill free?

Yes. It is published on GitHub by uphiago 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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