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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
69 lines
2.5 KiB
Markdown
69 lines
2.5 KiB
Markdown
# API Reference: Timeline Reconstruction with Plaso Agent
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## Overview
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Wraps Plaso (log2timeline/psort) via subprocess for forensic super-timeline generation, filtering, export, and automated CSV analysis for activity spikes and source distribution.
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## Dependencies
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| Package | Version | Purpose |
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|---------|---------|---------|
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| csv | stdlib | Timeline CSV parsing |
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| subprocess | stdlib | Plaso tool execution |
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## External Tools Required
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| Tool | Purpose |
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|------|---------|
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| log2timeline.py | Forensic timeline generation from disk images |
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| psort.py | Timeline filtering, sorting, and export |
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## Core Functions
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### `run_log2timeline(image_path, storage_file, parsers, filter_file)`
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Executes log2timeline.py to parse a disk image into a Plaso storage file.
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- **Parameters**: `image_path` (str), `storage_file` (str), `parsers` (str, optional), `filter_file` (str, optional)
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- **Timeout**: 7200 seconds (2 hours)
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- **Returns**: `dict` with command, returncode, stdout, stderr
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### `run_psort_export(storage_file, output_file, output_format, date_filter)`
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Exports timeline from Plaso storage to CSV, JSONL, or dynamic format.
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- **Formats**: `l2tcsv`, `json_line`, `dynamic`
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- **Returns**: `dict` with command, returncode, output_file
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### `create_filter_file(filter_path, paths)`
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Generates a Plaso filter file targeting key forensic artifacts.
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- **Default paths**: winevt, Prefetch, NTUSER.DAT, Chrome, Firefox, MFT, USN Journal, registry
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### `analyze_timeline_csv(csv_path, max_rows)`
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Statistical analysis of exported timeline: source distribution and hourly activity spikes (>3x average).
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- **Returns**: `dict` with `total_events`, `source_counts`, `spike_hours`, `avg_events_per_hour`
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### `generate_incident_window(storage_file, output_dir, start_date, end_date)`
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Exports events within a specific date range for focused analysis.
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### `full_pipeline(image_path, output_dir, parsers, start_date, end_date)`
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End-to-end pipeline: log2timeline -> psort export -> CSV analysis -> incident window -> JSONL export.
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## Default Parsers
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```
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winevtx, prefetch, mft, usnjrnl, lnk, recycle_bin,
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chrome_history, firefox_history, winreg
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```
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## Usage
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```bash
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python agent.py /cases/evidence.dd /cases/timeline/ "2024-01-15 00:00:00" "2024-01-20 23:59:59"
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```
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## Output Files
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| File | Format | Purpose |
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|------|--------|---------|
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| evidence.plaso | SQLite | Plaso intermediate storage |
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| full_timeline.csv | L2T CSV | Complete super-timeline |
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| incident_window.csv | L2T CSV | Filtered incident period |
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| timeline.jsonl | JSON Lines | SIEM/Timesketch import |
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