Portfan

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ portfan-emit --version
portfan 0.1.0

$ portfan-emit --help
usage: portfan [-h] [--version] [--format {table,json}] {triage,diff} ...

Summarize and diff nmap XML into prioritized triage findings (defensive
analysis only — no scanning, no network).

positional arguments:
  {triage,diff}
    triage              Summarize one nmap XML scan.
    diff                Diff two nmap XML scans.

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        Output format (default: table).

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

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

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Activity Detected",
        "description": "Anomalous network traffic detected from IP 192.168.1.100",
        "severity": "high",
        "created": "2023-02-20T14:30:00Z"
    }
]
}

Usage — step by step

1. Install the CLI (Python 3.9+):

   pip install git+https://github.com/cognis-digital/portfan.git
   

2. Run an nmap scan that emits XML, then triage it into prioritized findings:

   nmap -oX scan.xml -sV target.example.com
   portfan triage scan.xml
   

3. Get machine-readable output for dashboards or tickets:

   portfan --format json triage scan.xml > findings.json
   

4. Diff a new scan against a baseline to surface newly exposed services:

   portfan diff baseline.xml scan.xml
   

5. Track exposure drift in CI:

   - name: port exposure diff
     run: |
       pip install git+https://github.com/cognis-digital/portfan.git
       portfan --format json diff baseline.xml scan.xml
   

Contents

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

Why portfan?

turn raw scans into a triage list

portfan 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.

Features

• ✅ Score Service
• ✅ Parse Nmap Xml
• ✅ Summarize
• ✅ Diff Reports
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[capture / scan] --> P[portfan<br/>parse + map]
  P --> OUT[report]

Use it from any AI stack

portfan is interoperable with every popular way of using AI:

• MCP server — portfan mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe portfan 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

How it compares

	Cognis portfan	nmap
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

Built in the spirit of nmap, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

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

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/portfan.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/portfan.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/portfan.git" # uv
pip install cognis-portfan                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/portfan:latest --help        # Docker
brew install cognis-digital/tap/portfan                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/portfan/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/portfan	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• subhunt — Aggregate & dedupe subdomain enumeration from multiple sources
• dirsight — Analyze web content-discovery output (ffuf/gobuster) into ranked endpoints
• jwtinspect — Decode JWTs and lint for alg=none, weak secrets, and missing claims
• corsaudit — Detect permissive/misconfigured CORS from headers or a config
• headerscan — Grade HTTP security headers (CSP/HSTS/XFO) A-F from a response dump
• ssltriage — Grade TLS config (protocols/ciphers/expiry) from openssl/sslyze output

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

Contributing

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

⭐ If portfan 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 ([email protected]). See LICENSE.

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