Electionlens

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ electionlens-emit --version
electionlens 0.1.0

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

Influence-operations pattern monitor for election periods.

positional arguments:
  {scan}
    scan                scan a corpus of posts for coordinated inauthentic
                        behavior

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 electionlens output — reproduce them from a clone.

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

{
    "results": [
        {
            "id": "123456",
            "title": "Voter Registration Scam",
            "description": "Suspicious voter registration activity detected.",
            "confidence": 0.8,
            "tags": ["phishing", "voting"],
            "indicators": [
                {"type": "IP", "value": "192.0.2.1"},
                {"type": "URL", "value": "https://example.com"}
            ]
        },
        {
            "id": "789012",
            "title": "Social Media Disinformation Campaign",
            "description": "Malicious social media posts detected.",
            "confidence": 0.9,
            "tags": ["disinfo", "socialmedia"],
            "indicators": [
                {"type": "Twitter Handle", "value": "@FakeNews"},
                {"type": "Facebook Page", "value": "https://example.com/page"}
            ]
        }
    ]
}

Usage — step by step

1. Install the CLI:

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

2. Scan a corpus of posts for coordinated inauthentic behavior — the primary command. Accepts a JSON array, JSONL, or - for stdin:

   electionlens scan posts.jsonl
   cat posts.json | electionlens scan -
   

3. Tune the clustering — burst bucket size and the account counts that flag copypasta clusters and bursts:

   electionlens scan posts.jsonl \
     --window 120 --min-cluster-accounts 3 --min-burst-accounts 4
   

4. Read the output — table by default, or JSON for downstream analysis:

   electionlens --format json scan posts.jsonl > cib-report.json
   

5. Automate in CI / monitoring — exit non-zero (3) when the risk level is CRITICAL:

   electionlens scan posts.jsonl --fail-on-critical
   # exit 3 => CRITICAL coordinated behavior detected
   

Contents

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

Why electionlens?

Influence-operations pattern monitor for election periods — without standing up heavyweight infrastructure.

electionlens 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

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

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

electionlens is interoperable with every popular way of using AI:

• MCP server — electionlens mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe electionlens 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 electionlens	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 (electionlens 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/electionlens.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/electionlens.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/electionlens.git" # uv
pip install cognis-electionlens                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/electionlens:latest --help        # Docker
brew install cognis-digital/tap/electionlens                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/electionlens/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/electionlens	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• claimtrace — Misinformation provenance tracer — earliest-known appearance graph
• deepcheck — Lightweight synthetic-media detector with C2PA validation
• narrativediff — News bias & framing diff across 50+ outlets per event

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 electionlens 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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