SKILLS/analyzing-windows-prefetch-with-python/SKILL.md
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.
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-windows-prefetch-with-pythonInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
windowsprefetch library (pip install windowsprefetch)Gather .pf files from target system's C:\Windows\Prefetch\ directory.
Extract executable name, run count, last execution timestamps, and volume information.
Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.
Reconstruct chronological execution timeline from all Prefetch files.
JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.
$ 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
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.