/analyzing-prefetch-files-for-execution-history
Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-prefetch-files-for-execution-history --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/analyzing-prefetch-files-for-execution-history
Context preview
The summary Claude sees to decide when to auto-load this skill.
Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a
SKILL.md
analyzing-prefetch-files-for-execution-history.SKILL.mdname: analyzing-prefetch-files-for-execution-history
description: Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an investigation.
domain: cybersecurity
subdomain: digital-forensics
tags:
- forensics
- prefetch
- windows-artifacts
- execution-history
- timeline-analysis
- evidence-collection
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- RS.AN-03
- DE.AE-02
- RS.MA-01
mitre_attack:
- T1059.001
- T1003.001
- T1021.002
- T1567.002
Analyzing Prefetch Files for Execution History
When to Use
- When determining which programs were executed on a Windows system and when
- During malware investigations to confirm execution of suspicious binaries
- For establishing a timeline of application usage during an incident
- When correlating program execution with other forensic artifacts
- To identify anti-forensic tools or unauthorized software that was run
Prerequisites
- Access to Windows Prefetch directory (C:\Windows\Prefetch\) from forensic image
- PECmd (Eric Zimmerman), WinPrefetchView, or python-prefetch parser
- Understanding of Prefetch file format (versions 17, 23, 26, 30)
- Windows system with Prefetch enabled (default on client OS, disabled on servers)
- Knowledge of Prefetch naming conventions (APPNAME-HASH.pf)
Workflow
Step 1: Extract Prefetch Files from Forensic Image
# Mount the forensic image
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence
# Copy all prefetch files
mkdir -p /cases/case-2024-001/prefetch/
cp /mnt/evidence/Windows/Prefetch/*.pf /cases/case-2024-001/prefetch/
# Count and list prefetch files
ls -la /cases/case-2024-001/prefetch/ | wc -l
ls -la /cases/case-2024-001/prefetch/ | head -30
# Hash all prefetch files for integrity
sha256sum /cases/case-2024-001/prefetch/*.pf > /cases/case-2024-001/prefetch/pf_hashes.txt
# Note: Prefetch filename format is EXECUTABLE_NAME-XXXXXXXX.pf
# The hash (XXXXXXXX) is based on the executable path
# Same executable from different paths creates different prefetch files
Step 2: Parse Prefetch Files with PECmd
# Using Eric Zimmerman's PECmd (Windows or via Mono/Wine on Linux)
# Download from https://ericzimmerman.github.io/
# Parse a single prefetch file
PECmd.exe -f "C:\cases\prefetch\POWERSHELL.EXE-A]B2C3D4.pf"
# Parse all prefetch files and output to CSV
PECmd.exe -d "C:\cases\prefetch\" --csv "C:\cases\analysis\" --csvf prefetch_results.csv
# Parse with JSON output
PECmd.exe -d "C:\cases\prefetch\" --json "C:\cases\analysis\" --jsonf prefetch_results.json
# Output includes for each file:
# - Executable name and path
# - Run count
# - Last run time (up to 8 timestamps in Windows 10)
# - Files and directories referenced during execution
# - Volume information (serial number, creation date)
# - Prefetch file creation time
Step 3: Parse with Python for Linux-Based Analysis
pip install prefetch
python3 << 'PYEOF'
import os
import json
from datetime import datetime
# Parse prefetch files using python
import struct
def parse_prefetch(filepath):
"""Parse a Windows Prefetch file."""
with open(filepath, 'rb') as f:
data = f.read()
# Check for MAM compressed format (Windows 10)
if data[:4] == b'MAM\x04':
import lznt1 # or use DecompressBuffer
# Windows 10 prefetch files are compressed
print(f" [Compressed Win10 format - use PECmd for full parsing]")
return None
# Version 17 (XP), 23 (Vista/7), 26 (8.1), 30 (10)
version = struct.unpack('<I', data[0:4])[0]
signature = data[4:8]
if signature != b'SCCA':
print(f" Invalid prefetch signature")
return None
file_size = struct.unpack('<I', data[8:12])[0]
exec_name = data[16:76].decode('utf-16-le').strip('\x00')
run_count = struct.unpack('<I', data[208:212])[0] if version >= 23 else struct.unpack('<I', data[144:148])[0]
result = {
'version': version,
'executable': exec_name,
'file_size': file_size,
'run_count': run_count,
}
# Extract last execution timestamps
if version == 23: # Vista/7 - 1 timestamp
ts = struct.unpack('<Q', data[128:136])[0]
result['last_run'] = filetime_to_datetime(ts)
elif version >= 26: # Win8+ - up to 8 timestamps
timestamps = []
for i in range(8):
ts = struct.unpack('<Q', data[128+i*8:136+i*8])[0]
if ts > 0:
timestamps.append(filetime_to_datetime(ts))
result['last_run_times'] = timestamps
return result
def filetime_to_datetime(ft):
"""Convert Windows FILETIME to datetime string."""
if ft == 0:
return None
timestamp = (ft - 116444736000000000) / 10000000
try:
return datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')
except (OSError, ValueError):
return None
# Process all prefetch files
prefetch_dir = '/cases/case-2024-001/prefetch/'
results = []
for filename in sorted(os.listdir(prefetch_dir)):
if filename.lower().endswith('.pf'):
filepath = os.path.join(prefetch_dir, filename)
print(f"\n=== {filename} ===")
result = parse_prefetch(filepath)
if result:
print(f" Executable: {result['executable']}")
print(f" Run Count: {result['run_count']}")
if 'last_run' in result:
print(f" Last Run: {result['last_run']}")
elif 'last_run_times' in result:
for i, ts in enumerate(result['last_run_times']):
print(f" Run Time {i+1}: {ts}")
results.appRead more
name: analyzing-prefetch-files-for-execution-history description: Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an investigation. domain: cybersecurity subdomain: digital-forensics tags: - forensics - prefetch - windows-artifacts - execution-history - timeline-analysis - evidence-collection version: '1.0' author: mahipal license: Apache-2.0 nist_csf: - RS.AN-03 - DE.AE-02 - RS.MA-01 mitre_attack: - T1059.001 - T1003.001 - T1021.002 - T1567.002
Analyzing Prefetch Files for Execution History
When to Use
- When determining which programs were executed on a Windows system and when
- During malware investigations to confirm execution of suspicious binaries
- For establishing a timeline of application usage during an incident
- When correlating program execution with other forensic artifacts
- To identify anti-forensic tools or unauthorized software that was run
Prerequisites
- Access to Windows Prefetch directory (C:\Windows\Prefetch\) from forensic image
- PECmd (Eric Zimmerman), WinPrefetchView, or python-prefetch parser
- Understanding of Prefetch file format (versions 17, 23, 26, 30)
- Windows system with Prefetch enabled (default on client OS, disabled on servers)
- Knowledge of Prefetch naming conventions (APPNAME-HASH.pf)
Workflow
Step 1: Extract Prefetch Files from Forensic Image
# Mount the forensic image mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence # Copy all prefetch files mkdir -p /cases/case-2024-001/prefetch/ cp /mnt/evidence/Windows/Prefetch/*.pf /cases/case-2024-001/prefetch/ # Count and list prefetch files ls -la /cases/case-2024-001/prefetch/ | wc -l ls -la /cases/case-2024-001/prefetch/ | head -30 # Hash all prefetch files for integrity sha256sum /cases/case-2024-001/prefetch/*.pf > /cases/case-2024-001/prefetch/pf_hashes.txt # Note: Prefetch filename format is EXECUTABLE_NAME-XXXXXXXX.pf # The hash (XXXXXXXX) is based on the executable path # Same executable from different paths creates different prefetch files
Step 2: Parse Prefetch Files with PECmd
# Using Eric Zimmerman's PECmd (Windows or via Mono/Wine on Linux) # Download from https://ericzimmerman.github.io/ # Parse a single prefetch file PECmd.exe -f "C:\cases\prefetch\POWERSHELL.EXE-A]B2C3D4.pf" # Parse all prefetch files and output to CSV PECmd.exe -d "C:\cases\prefetch\" --csv "C:\cases\analysis\" --csvf prefetch_results.csv # Parse with JSON output PECmd.exe -d "C:\cases\prefetch\" --json "C:\cases\analysis\" --jsonf prefetch_results.json # Output includes for each file: # - Executable name and path # - Run count # - Last run time (up to 8 timestamps in Windows 10) # - Files and directories referenced during execution # - Volume information (serial number, creation date) # - Prefetch file creation time
Step 3: Parse with Python for Linux-Based Analysis
pip install prefetch
python3 << 'PYEOF'
import os
import json
from datetime import datetime
# Parse prefetch files using python
import struct
def parse_prefetch(filepath):
"""Parse a Windows Prefetch file."""
with open(filepath, 'rb') as f:
data = f.read()
# Check for MAM compressed format (Windows 10)
if data[:4] == b'MAM\x04':
import lznt1 # or use DecompressBuffer
# Windows 10 prefetch files are compressed
print(f" [Compressed Win10 format - use PECmd for full parsing]")
return None
# Version 17 (XP), 23 (Vista/7), 26 (8.1), 30 (10)
version = struct.unpack('<I', data[0:4])[0]
signature = data[4:8]
if signature != b'SCCA':
print(f" Invalid prefetch signature")
return None
file_size = struct.unpack('<I', data[8:12])[0]
exec_name = data[16:76].decode('utf-16-le').strip('\x00')
run_count = struct.unpack('<I', data[208:212])[0] if version >= 23 else struct.unpack('<I', data[144:148])[0]
result = {
'version': version,
'executable': exec_name,
'file_size': file_size,
'run_count': run_count,
}
# Extract last execution timestamps
if version == 23: # Vista/7 - 1 timestamp
ts = struct.unpack('<Q', data[128:136])[0]
result['last_run'] = filetime_to_datetime(ts)
elif version >= 26: # Win8+ - up to 8 timestamps
timestamps = []
for i in range(8):
ts = struct.unpack('<Q', data[128+i*8:136+i*8])[0]
if ts > 0:
timestamps.append(filetime_to_datetime(ts))
result['last_run_times'] = timestamps
return result
def filetime_to_datetime(ft):
"""Convert Windows FILETIME to datetime string."""
if ft == 0:
return None
timestamp = (ft - 116444736000000000) / 10000000
try:
return datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')
except (OSError, ValueError):
return None
# Process all prefetch files
prefetch_dir = '/cases/case-2024-001/prefetch/'
results = []
for filename in sorted(os.listdir(prefetch_dir)):
if filename.lower().endswith('.pf'):
filepath = os.path.join(prefetch_dir, filename)
print(f"\n=== {filename} ===")
result = parse_prefetch(filepath)
if result:
print(f" Executable: {result['executable']}")
print(f" Run Count: {result['run_count']}")
if 'last_run' in result:
print(f" Last Run: {result['last_run']}")
elif 'last_run_times' in result:
for i, ts in enumerate(result['last_run_times']):
print(f" Run Time {i+1}: {ts}")
results.app817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
Other skills on cybersecurity-skills.
- /abusing-dpapi-for-credential-access
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use
Open skill - /abusing-shadow-credentials-for-privesc
Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows
Open skill - /achieving-cmmc-level-2-compliance
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the
Open skill - /acquiring-disk-image-with-dd-and-dcfldd
Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving
Open skill - /analyzing-active-directory-acl-abuse
Detect dangerous ACL misconfigurations in Active Directory using ldap3
Open skill - /analyzing-android-malware-with-apktool
Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and
Open skill

