Sigmeta

pip install cognis-sigmeta

sigmeta classify capture.log         # → normalized signal catalog in ms

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ sigmeta-emit --version
sigmeta 0.6.6

$ sigmeta-emit --help
usage: sigmeta [-h] [--version] [--format {table,json,csv}] {classify} ...

Parse and classify signal metadata into a normalized catalog.

positional arguments:
  {classify}
    classify            parse a signal log into a normalized catalog

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

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

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

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Traffic",
        "description": "Anomalous network traffic detected from IP 192.168.1.100",
        "created_by": "John Doe",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Malware Detection",
        "description": "Malware detected on machine with IP 192.168.1.101",
        "created_by": "Jane Smith",
        "created_at": "2023-02-16T10:45:00Z"
    }
]
}

Contents

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

Usage — step by step

sigmeta parses textual signal-metadata logs into a normalized catalog (frequency, band, modulation, bandwidth, service hint). Analysis-only — no transmit or hardware control. Exits 1 when no records parse.

1. Install

   pip install sigmeta
   

2. Classify a log file into a catalog table:

   sigmeta classify capture.log
   

3. Pipe from stdin (the input arg defaults to -):

   cat capture.log | sigmeta classify -
   

4. Read JSON output for downstream processing, CSV for spreadsheets/SIEM, or just the rollup with --summary-only:

   sigmeta --format json classify capture.log | jq '.summary'
   sigmeta --format csv  classify capture.log > catalog.csv
   sigmeta classify capture.log --summary-only
   

5. Gate automation/CI — use --strict to fail on the first unparseable line, and rely on the non-zero exit when nothing parses:

   sigmeta classify capture.log --strict || echo "no usable signal records"
   

Why sigmeta?

Parse and classify signal metadata (freq, modulation, bandwidth) into a normalized catalog. — without standing up heavyweight infrastructure.

sigmeta is single-purpose, scriptable, and self-hostable: point it at a log, get a normalized catalog in the format your workflow already speaks (table · JSON · CSV), gate CI on the exit code, and let agents drive it over MCP.

Features

• ✅ Normalize Modulation

• ✅ Classify Band

• ✅ Service Hint

• ✅ Parse Line

• ✅ Parse Lines

• ✅ Catalog Summary

• ✅ Table · JSON · CSV output (--format csv for spreadsheets/SIEM)

• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer

• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-sigmeta

sigmeta --version

sigmeta classify capture.log                 # normalized catalog (table)

sigmeta --format json classify capture.log   # machine-readable

sigmeta --format csv  classify capture.log   # spreadsheet / SIEM import

cat capture.log | sigmeta classify -         # stdin

sigmeta classify capture.log --strict        # CI gate (non-zero exit)

Example

$ sigmeta classify demos/02-airband-scan/airband.log

LABEL      FREQ        BAND  MOD  BW        SERVICE HINT
---------  ----------  ----  ---  --------  -----------------------------
TWR        118.3 MHz   VHF   AM   8.33 kHz  Aeronautical (AM voice / nav)
GND        121.9 MHz   VHF   AM   25 kHz    Aeronautical (AM voice / nav)
EMERGENCY  121.5 MHz   VHF   AM   25 kHz    Aeronautical (AM voice / nav)

records=6  bands={'VHF': 6}  mods={'AM': 6}  warnings=0

Try the demos

demos/ ships realistic, ready-to-run signal logs in the tool's real input format, each with a SCENARIO.md (where the data came from, the exact run command, and what to expect):

Demo	Scenario
01-basic	Heterogeneous mixed-style log (kv + positional, mixed units)
02-airband-scan	VHF aeronautical airband (118-137 MHz AM)
03-ism-survey	ISM / U-NII unlicensed-band spectrum survey
04-hf-utility	HF utility / shortwave monitoring (3-30 MHz)
05-empty-strict	No usable records → exit-code / CI gate
06-marine-vhf	Marine VHF channel plan (156-162 MHz)
07-pmr-landmobile	UHF land-mobile / FRS-GMRS / DMR inventory
08-satellite-downlink	Satellite / weather downlink tracking
09-mixed-units-csv	Every unit/style → --format csv export

python -m sigmeta classify demos/03-ism-survey/ism.log
python -m sigmeta --format csv classify demos/09-mixed-units-csv/capture.log

Architecture

flowchart LR
  IN[sources] --> P[sigmeta<br/>curate + validate]
  P --> OUT[query / analysis]

Use it from any AI stack

sigmeta is interoperable with every popular way of using AI:

• MCP server — sigmeta mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)

• OpenAI-compatible / JSON — pipe sigmeta --format json classify capture.log into any agent or LLM

• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line

• CI / scripts — exit codes + CSV/JSON for non-AI pipelines

How it compares

| | Cognis sigmeta | typical tools |

|---|:---:|:---:|

| Self-hostable, no account | ✅ | varies |

| Single command, zero config | ✅ | ⚠️ |

| JSON + CSV for CI | ✅ | varies |

| MCP-native (AI agents) | ✅ | ❌ |

| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |

| Open license | ✅ COCL | varies |

Integrations

Pipes into your stack: CSV for spreadsheets/SIEM, JSON for anything, an MCP server (sigmeta 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/sigmeta.git"    # pip (works today)

pipx install "git+https://github.com/cognis-digital/sigmeta.git"   # isolated CLI

uv tool install "git+https://github.com/cognis-digital/sigmeta.git" # uv

pip install cognis-sigmeta                                          # PyPI (when published)

docker run --rm ghcr.io/cognis-digital/sigmeta:latest --help        # Docker

brew install cognis-digital/tap/sigmeta                             # Homebrew tap

curl -fsSL https://raw.githubusercontent.com/cognis-digital/sigmeta/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/sigmeta | 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 sigmeta 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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