Interpret Fields in Maigret JSON Output

JSON exports let you feed Maigret findings into other systems.

Sample structure

{
  "query_username": "exampleuser",
  "matches": [
    {
      "site": "GitHub",
      "url_main": "https://github.com",
      "url_user": "https://github.com/exampleuser",
      "status": "FOUND",
      "score": 1.0,
      "ids": {
        "name": "Example User",
        "followers": 128
      }
    }
  ],
  "not_found": ["Twitter", "Medium"],
  "disabled": ["Pinterest"]
}

Key fields:

• query_username: the handle you searched.
• matches: array of confirmed hits.
• status: FOUND, NOT_FOUND, or UNKNOWN.
• score: confidence (0–1) based on rule strength.
• ids: structured attributes extracted from the profile HTML.

Consuming in Python

import json
from pathlib import Path

data = json.loads(Path("reports/exampleuser.json").read_text())
for match in data["matches"]:
    print(match["site"], match["score"], match["ids"].get("name"))

Building a knowledge graph

  graph LR
    A[Maigret JSON] --> B[ETL Script]
    B --> C[Neo4j]
    C --> D[Investigations UI]

Use the ids dictionary to create properties on nodes/edges. Pay attention to:

• username_norm: lowercased variant for deduplication.
• date: timestamp when the evidence was collected.
• mark: optional engine markers indicating detection mode.

Normalisation tips

• Always log the maigret_version to track schema changes.
• Strip HTML from any bio fields before indexing.
• Combine with --folderoutput to keep historical snapshots per username.

Understanding the schema keeps downstream tooling accurate and auditable.