analyzing-windows-prefetch-with-python

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

16 stars

Best use case

analyzing-windows-prefetch-with-python is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

Teams using analyzing-windows-prefetch-with-python should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/analyzing-windows-prefetch-with-python/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/analyzing-windows-prefetch-with-python/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/analyzing-windows-prefetch-with-python/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How analyzing-windows-prefetch-with-python Compares

Feature / Agentanalyzing-windows-prefetch-with-pythonStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Analyzing Windows Prefetch with Python

## Overview

Windows Prefetch files (.pf) record application execution data including executable names, run counts, timestamps, loaded DLLs, and accessed directories. This skill covers parsing Prefetch files using the windowsprefetch Python library to reconstruct execution timelines, detect renamed or masquerading binaries by comparing executable names with loaded resources, and identifying suspicious programs that may indicate malware execution or lateral movement.


## When to Use

- When investigating security incidents that require analyzing windows prefetch with python
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Python 3.9+ with `windowsprefetch` library (pip install windowsprefetch)
- Windows Prefetch files from C:\Windows\Prefetch\ (versions 17-30 supported)
- Understanding of Windows Prefetch file naming conventions (EXECUTABLE-HASH.pf)

## Steps

### Step 1: Collect Prefetch Files
Gather .pf files from target system's C:\Windows\Prefetch\ directory.

### Step 2: Parse Execution History
Extract executable name, run count, last execution timestamps, and volume information.

### Step 3: Detect Suspicious Execution
Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.

### Step 4: Build Execution Timeline
Reconstruct chronological execution timeline from all Prefetch files.

## Expected Output

JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.

## Example Output

```text
$ python3 prefetch_analyzer.py --dir /evidence/Windows/Prefetch --output /analysis/prefetch_report

Windows Prefetch Analyzer v2.1
================================
Source: /evidence/Windows/Prefetch/
Prefetch Format: Windows 10 (MAM compressed, version 30)
Files Found: 234

--- Execution Timeline (Incident Window: 2024-01-15 to 2024-01-18) ---
Last Executed (UTC)     | Run Count | Filename                    | Hash     | Path
------------------------|-----------|-----------------------------|----------|------------------------------------------
2024-01-15 14:33:15     | 1         | Q4_REPORT.XLSM-2A1B3C4D.pf | 2A1B3C4D | C:\Users\jsmith\Downloads\Q4_Report.xlsm
2024-01-15 14:35:44     | 1         | POWERSHELL.EXE-A2B3C4D5.pf  | A2B3C4D5 | C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe
2024-01-15 14:36:30     | 3         | UPDATE_CLIENT.EXE-B3C4D5E6.pf| B3C4D5E6| C:\ProgramData\Updates\update_client.exe
2024-01-15 15:10:22     | 1         | NETSCAN.EXE-C4D5E6F7.pf     | C4D5E6F7 | C:\Users\jsmith\Downloads\netscan.exe
2024-01-16 02:28:00     | 1         | PROCDUMP64.EXE-D5E6F7A8.pf  | D5E6F7A8 | C:\Windows\Temp\procdump64.exe
2024-01-16 02:30:15     | 2         | MIMIKATZ.EXE-E6F7A8B9.pf    | E6F7A8B9 | C:\Windows\Temp\mimikatz.exe
2024-01-16 02:40:00     | 4         | PSEXEC.EXE-F7A8B9C0.pf      | F7A8B9C0 | C:\Users\jsmith\AppData\Local\Temp\psexec.exe
2024-01-17 02:45:00     | 1         | SDELETE64.EXE-A8B9C0D1.pf   | A8B9C0D1 | C:\Windows\Temp\sdelete64.exe
2024-01-18 03:00:45     | 1         | WEVTUTIL.EXE-B9C0D1E2.pf    | B9C0D1E2 | C:\Windows\System32\wevtutil.exe

--- Renamed Binary Detection ---
ALERT: UPDATE_CLIENT.EXE loaded DLLs consistent with Cobalt Strike beacon:
  Referenced DLLs: wininet.dll, ws2_32.dll, advapi32.dll, dnsapi.dll, netapi32.dll
  Volume: \VOLUME{01d94f2a3b5c7d8e-A4E73F21} (C:)
  Directories referenced:
    C:\ProgramData\Updates\
    C:\Windows\System32\

--- Execution Frequency Analysis ---
Most Executed (Top 5):
  1. SVCHOST.EXE          (267 runs)
  2. CHROME.EXE           (189 runs)
  3. EXPLORER.EXE         (156 runs)
  4. RUNTIMEBROKER.EXE    (134 runs)
  5. OUTLOOK.EXE          (98 runs)

First-Time Executions (Never seen before incident window):
  6 executables first run between 2024-01-15 and 2024-01-18

Summary:
  Total prefetch files:         234
  Suspicious executables:       6
  Renamed binary indicators:    1 (update_client.exe)
  Anti-forensics tools:         2 (sdelete64.exe, wevtutil.exe)
  JSON report: /analysis/prefetch_report/prefetch_timeline.json
```

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