Ewcorr

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ ewcorr-emit --version
ewcorr 0.1.0

$ ewcorr-emit --help
usage: ewcorr [-h] [--version] [--format {table,json}] {correlate} ...

Correlate passive EW/ELINT event logs into candidate emitter clusters
(defensive analysis / monitoring only).

positional arguments:
  {correlate}
    correlate           cluster an observation log into emitters

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 ewcorr output — reproduce them from a clone.

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

{
"timestamp": "2023-02-16T14:30:00Z",
"platform": {
"type": "STIX",
"url": "https://example.com/stix",
"token": "my_stix_token"
},
"findings": [
{
"id": "1",
"name": "Suspicious DNS Query",
"description": "DNS query for suspicious domain",
"indicators": [
{
"type": "dns",
"value": "example.com"
}
]
},
{
"id": "2",
"name": "Malware Detection",
"description": "Detection of malware on system",
"indicators": [
{
"type": "file",
"value": "/path/to/malware.exe"
}
]
}
]

Usage — step by step

1. Install the CLI:

   pipx install "git+https://github.com/cognis-digital/ewcorr.git"
   

2. Correlate an observation log into distinct emitters (primary command). Pass a CSV path or - for stdin:

   ewcorr correlate detections.csv
   cat detections.csv | ewcorr correlate -
   

3. Tune the clustering — link detections within a time window, frequency tolerance, and bearing tolerance:

   ewcorr correlate detections.csv \
     --time-window 45 --freq-tol 0.25 --bearing-tol 3
   

4. Read the output — table by default, or JSON for downstream tools; drop sparse emitters with --min-hits:

   ewcorr --format json correlate detections.csv --min-hits 3 > emitters.json
   

5. Automate in a pipeline — count correlated emitters from a feed:

   ewcorr --format json correlate feed.csv | jq 'length'
   

Contents

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

Why ewcorr?

Correlate electronic-warfare event logs by time/frequency/bearing to cluster emitters. — without standing up heavyweight infrastructure.

ewcorr 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

• ✅ Parse Observations
• ✅ Correlate
• ✅ Summarize
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[ewcorr<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

ewcorr is interoperable with every popular way of using AI:

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

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (ewcorr 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/ewcorr.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/ewcorr.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/ewcorr.git" # uv
pip install cognis-ewcorr                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/ewcorr:latest --help        # Docker
brew install cognis-digital/tap/ewcorr                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/ewcorr/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/ewcorr	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

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 ewcorr 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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