GITHUBRECON

Map a GitHub user/org footprint & leaked-secret surface from API exports

Part of the Cognis Neural Suite.

pip install cognis-githubrecon
githubrecon scan .            # → prioritized findings in seconds

Real, reproducible output from the tool — runs offline:

$ githubrecon-emit --version
githubrecon 0.1.0

$ githubrecon-emit --help
usage: githubrecon [-h] [--version] {scan} ...

Map a GitHub user/org footprint and leaked-secret surface from an API export
(defensive OSINT / forensics).

positional arguments:
  {scan}
    scan      analyze a GitHub API export file

options:
  -h, --help  show this help message and exit
  --version   show program's version number and exit

Blocks above are real githubrecon output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "1234567890",
        "title": "GitHub Reconnaissance Report",
        "description": "This report contains findings from a GitHub reconnaissance scan.",
        "objects": [
            {
                "id": "obj-1",
                "type": "github-repo",
                "name": "Example Repo",
                "owner": "johnDoe",
                "description": "A sample GitHub repository"
            },
            {
                "id": "obj-2",
                "type": "github-issue",
                "title": "Example Issue #1",
                "body": "This is an example issue on GitHub"
            }
        ]
    }
]
}

1. Install the analyzer:

   pip install cognis-githubrecon

2. Analyze an org/user export. githubrecon works offline against a JSON export of an account and its repos, flagging exposure (leaked emails, risky metadata, and more):

   githubrecon analyze export.json

3. Emit JSON or a standalone HTML report for sharing:

   githubrecon analyze export.json --format json | jq '.findings[] | select(.severity=="high")'
   githubrecon analyze export.json --format html > recon-report.html

4. Read the result. The table summarizes owner, repo/contributor/email counts, and findings-by-severity (critical/high/medium/low/info); JSON carries each finding's rule_id, repo, location, and evidence.

5. Automate in CI. Generate the report as a build artifact:

   githubrecon analyze export.json --format json > recon.json

• Why githubrecon? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Map a GitHub user/org footprint & leaked-secret surface from API exports — without standing up heavyweight infrastructure.

githubrecon is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

• ✅ Load Export
• ✅ Analyze
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

pip install cognis-githubrecon
githubrecon --version
githubrecon scan .                       # scan current project
githubrecon scan . --format json         # machine-readable
githubrecon scan . --fail-on high        # CI gate (non-zero exit)

$ githubrecon scan .
  [HIGH    ] GIT-001  example finding             (./src/app.py)
  [MEDIUM  ] GIT-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

flowchart LR
  IN[target / export] --> P[githubrecon<br/>collect + correlate]
  P --> OUT[ranked findings]

githubrecon is interoperable with every popular way of using AI:

• MCP server — githubrecon mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe githubrecon scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (githubrecon mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

pip install "git+https://github.com/cognis-digital/githubrecon.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/githubrecon.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/githubrecon.git" # uv
pip install cognis-githubrecon                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/githubrecon:latest --help        # Docker
brew install cognis-digital/tap/githubrecon                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/githubrecon/main/install.sh | sh

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If githubrecon saved you time, star it — it genuinely helps others find it.

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.