Claimtrace

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ claimtrace-emit --version
claimtrace 0.1.0

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

CLAIMTRACE - trace the earliest-known appearance of a claim across sources and
reconstruct its spread.

positional arguments:
  {trace}
    trace               trace provenance from a JSON observations file

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 claimtrace output — reproduce them from a clone.

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

{
"claimtrace": {
"finding": [
{
"id": "1234567890",
"name": "Suspicious Network Traffic",
"description": "Unusual network traffic detected from IP 192.168.1.100",
"severity": "medium",
"category": "network"
},
{
"id": "2345678901",
"name": "Malware Detection",
"description": "Malware detected on system with ID 1234567890",
"severity": "high",
"category": "endpoint"
}
]
}
}

Usage — step by step

claimtrace traces the earliest-known provenance of a claim from a JSON observations file.

1. Install:

   pip install -e .
   

2. Trace provenance from an observations file:

   claimtrace trace observations.json
   

3. Pipe observations via stdin (the input argument defaults to -):

   cat observations.json | claimtrace trace
   

4. Read the output as JSON for downstream analysis:

   claimtrace --format json trace observations.json
   

5. Automate in CI — fold a provenance check into a pipeline:

   claimtrace --format json trace observations.json > provenance.json
   

Contents

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

Why claimtrace?

Misinformation provenance tracer — earliest-known appearance graph — without standing up heavyweight infrastructure.

claimtrace 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
• ✅ Build Graph
• ✅ Trace
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

claimtrace is interoperable with every popular way of using AI:

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

Related Cognis tools

• deepcheck — Lightweight synthetic-media detector with C2PA validation
• electionlens — Influence-operations pattern monitor for election periods
• 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 claimtrace 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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