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-pythonInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
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.
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 foundOur 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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| analyzing-windows-prefetch-with-python (this skill)by mukul975 | 88 | 33.3k | 25d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.4k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.8k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 83.7k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 3d ago | SKILL.md |
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.
Skill content
View source on GitHubname: 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
windowsprefetchlibrary (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
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
