mirror of https://github.com/SHOGGOTH-SECTOR/sica-fondt.git synced 2026-08-01 08:30:20 +00:00

Consolidate 22 sibling repos into layered organism structure

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

2026-06-10 06:53:01 +00:00

2.5 KiB

Raw Blame History

API Reference: Timeline Reconstruction with Plaso Agent

Overview

Wraps Plaso (log2timeline/psort) via subprocess for forensic super-timeline generation, filtering, export, and automated CSV analysis for activity spikes and source distribution.

Dependencies

Package	Version	Purpose
csv	stdlib	Timeline CSV parsing
subprocess	stdlib	Plaso tool execution

External Tools Required

Tool	Purpose
log2timeline.py	Forensic timeline generation from disk images
psort.py	Timeline filtering, sorting, and export

Core Functions

`run_log2timeline(image_path, storage_file, parsers, filter_file)`

Executes log2timeline.py to parse a disk image into a Plaso storage file.

Parameters: image_path (str), storage_file (str), parsers (str, optional), filter_file (str, optional)
Timeout: 7200 seconds (2 hours)
Returns: dict with command, returncode, stdout, stderr

`run_psort_export(storage_file, output_file, output_format, date_filter)`

Exports timeline from Plaso storage to CSV, JSONL, or dynamic format.

Formats: l2tcsv, json_line, dynamic
Returns: dict with command, returncode, output_file

`create_filter_file(filter_path, paths)`

Generates a Plaso filter file targeting key forensic artifacts.

Default paths: winevt, Prefetch, NTUSER.DAT, Chrome, Firefox, MFT, USN Journal, registry

`analyze_timeline_csv(csv_path, max_rows)`

Statistical analysis of exported timeline: source distribution and hourly activity spikes (>3x average).

Returns: dict with total_events, source_counts, spike_hours, avg_events_per_hour

`generate_incident_window(storage_file, output_dir, start_date, end_date)`

Exports events within a specific date range for focused analysis.

`full_pipeline(image_path, output_dir, parsers, start_date, end_date)`

End-to-end pipeline: log2timeline -> psort export -> CSV analysis -> incident window -> JSONL export.

Default Parsers

winevtx, prefetch, mft, usnjrnl, lnk, recycle_bin,
chrome_history, firefox_history, winreg

Usage

python agent.py /cases/evidence.dd /cases/timeline/ "2024-01-15 00:00:00" "2024-01-20 23:59:59"

Output Files

File	Format	Purpose
evidence.plaso	SQLite	Plaso intermediate storage
full_timeline.csv	L2T CSV	Complete super-timeline
incident_window.csv	L2T CSV	Filtered incident period
timeline.jsonl	JSON Lines	SIEM/Timesketch import

2.5 KiB Raw Blame History