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analyzing-windows-prefetch-with-python

Parse Windows Prefetch (.pf) files with the windowsprefetch Python

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-prefetch-with-python

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Category

Security

Supported Platforms

Universal

Our assessment of analyzing-windows-prefetch-with-python

analyzing-windows-prefetch-with-python scores 88/100 on our quality scale, 293rd of 544 Security skills we index.

Its SKILL.md is 4.9 KB long, well organised into 11 sections with 1 code example: a solid amount of guidance for an agent.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so analyzing-windows-prefetch-with-python is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

analyzing-windows-prefetch-with-python compared with similar skills

All 4 of these similar skills score higher than analyzing-windows-prefetch-with-python; compare them before choosing.

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analyzing-windows-prefetch-with-python (this skill)by mukul9758833.3k25d agoSKILL.md
Agent-Reachby Panniantong10085.4k10d agoCLAUDE.md
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Scraplingby D4Vinci10083.7ktodayMCP Server
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Frequently asked questions

How do I install analyzing-windows-prefetch-with-python?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-prefetch-with-python. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-windows-prefetch-with-python work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-windows-prefetch-with-python safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-windows-prefetch-with-python still maintained?
The repository was last updated 25 days ago, so analyzing-windows-prefetch-with-python is actively maintained.

name: analyzing-windows-prefetch-with-python description: Parse Windows Prefetch (.pf) files with the windowsprefetch Python library to reconstruct application execution history, run counts, and accessed file/volume lists. Use when investigating renamed or masquerading binaries, verifying program execution timelines, or hunting for suspicious execution patterns in incident response. domain: cybersecurity subdomain: digital-forensics tags:

  • digital-forensics
  • windows
  • prefetch
  • execution-history
  • incident-response
  • malware-analysis mitre_attack:
  • T1036.005
  • T1070.004
  • T1070
  • T1003.001
  • T1057 version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • RS.AN-03
  • DE.AE-02
  • RS.MA-01

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

$ 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

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions