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mukul975
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Complete skill folder anatomy across all cybersecurity skills: - scripts/agent.py: 80-150 line Python agents using real libraries (impacket, boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.) - references/api-reference.md: real API documentation with method signatures - LICENSE: MIT license for all skill folders
2.0 KiB
2.0 KiB
API Reference: Diamond Model Analysis Agent
Dependencies
| Library | Version | Purpose |
|---|---|---|
| Python stdlib | 3.8+ | json, dataclasses, hashlib, argparse |
CLI Usage
python scripts/agent.py \
--input events.json \
--output diamond_report.json \
--pivot-type infrastructure \
--pivot-value "185.220.101.42"
Input Format
[
{
"event_id": "EVT-001",
"timestamp": "2025-01-15T14:30:00Z",
"adversary": ["APT29"],
"adversary_confidence": "high",
"capabilities": ["SUNBURST", "T1071.001"],
"infrastructure": ["185.220.101.42", "evil-redir.com"],
"victims": ["TargetCorp"],
"phase": "C2",
"result": "success"
}
]
Functions
create_event(event_data) -> DiamondEvent
Constructs a DiamondEvent dataclass from raw dict. Auto-generates event_id via MD5 if not provided.
pivot_on_vertex(events, vertex_type, value) -> list
Returns events sharing a specified vertex value. Supports pivoting on adversary, capability, infrastructure, victim.
cluster_events(events) -> dict
Groups events by shared infrastructure or capability values. Returns clusters with overlapping event IDs.
build_activity_thread(events) -> list
Sorts events chronologically and assigns sequence numbers for timeline reconstruction.
generate_report(events) -> dict
Produces the full Diamond Model report with unique entities, activity thread, and clusters.
Data Classes
Vertex
Fields: vertex_type (str), values (list), confidence (str), notes (str)
DiamondEvent
Fields: event_id, timestamp, adversary (Vertex), capability (Vertex), infrastructure (Vertex), victim (Vertex), phase, direction, result
Output Schema
{
"report_date": "ISO-8601",
"total_events": 5,
"unique_adversaries": ["APT29"],
"unique_infrastructure": ["185.220.101.42"],
"activity_thread": [{"sequence": 1, "event_id": "EVT-001", ...}],
"clusters": {"clusters": [...], "total_events": 5}
}