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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
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1.1 KiB
API Reference — Performing OT Vulnerability Assessment with Claroty
Libraries Used
- requests: HTTP client for Claroty xDome REST API
CLI Interface
python agent.py --url <claroty_url> --token <api_token> vulns [--severity critical] [--limit 100]
python agent.py --url <claroty_url> --token <api_token> prioritize
python agent.py --url <claroty_url> --token <api_token> risk
ClarotyVulnClient API
get_vulnerabilities(severity, asset_type, limit)
Endpoint: GET /api/v1/vulnerabilities
get_affected_assets(cve_id)
Endpoint: GET /api/v1/vulnerabilities/cve/{cve_id}/assets
get_risk_score()
Endpoint: GET /api/v1/risk/score
Core Functions
assess_vulnerabilities(...) — Analyze OT vulnerability landscape
Groups by severity and asset type. Extracts critical vulnerabilities with CVSS scores.
prioritize_remediation(...) — Risk-based prioritization
Risk formula: CVSS * asset_criticality * exploitability / 10
Priority: CRITICAL (>=4), HIGH (>=2.5), MEDIUM (>=1.5), LOW (<1.5).
get_risk_overview(...) — Overall OT risk posture
Dependencies
pip install requests