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

Usage — step by step

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

Contents

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

Why githubrecon?

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.

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Features

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

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Quick start

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)

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Example

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

  2 findings · risk score 5 · 38ms

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Architecture

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

Loading

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Use it from any AI stack

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

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How it compares

	Cognis githubrecon	typical tools
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

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Integrations

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.

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Install — every way, every platform

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

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/githubrecon	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

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

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Contributing

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.

Interoperability

{} 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.

License

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.

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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}