analyzing-windows-prefetch-with-python

analyzing-windows-prefetch-with-python is a skill for Claude Code, Codex from Youngmaidainon/Agent-Level-Up. It costs 66 tokens per session (1,487 once invoked), scanned A, a copy of analyzing-windows-prefetch-with-python, MIT.

A Python-based forensic workflow for reading Windows Prefetch files, which record information about programs that have run. The records can include run counts, timestamps, loaded libraries, and accessed locations.

In plain words
What is it for?
Use it during incident response and threat hunting to investigate program execution, renamed binaries, suspicious activity, and lateral movement.
Why use it?
It helps confirm whether a program ran and build an execution timeline, including clues that a suspicious file was renamed to look harmless.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/youngmaidainon/agent-level-up/analyzing-windows-prefetch-with-python
Any agent
npx skills add Youngmaidainon/Agent-Level-Up --skill analyzing-windows-prefetch-with-python
Clone the repo
git clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-Up

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-windows-prefetch-with-python

README.md
[![agentmods](https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/analyzing-windows-prefetch-with-python.svg)](https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-windows-prefetch-with-python)
Your own site
<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-windows-prefetch-with-python"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/analyzing-windows-prefetch-with-python.svg" alt="Measured on agentmods" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00066 $0.01487
Opus 5 $0.00033 $0.00744
Sonnet 5 $0.00013 $0.00297
Haiku 4.5 $0.00007 $0.00149

Measured 2d ago against content hash 758ac842d790, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

analyzing-windows-prefetch-with-python scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to analyzing-windows-prefetch-with-python — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

cyber-security/ctf/analyzing-windows-prefetch-with-python/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 119 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 119 lines · 66 tokens per session scan A 758ac842d790

Subscribe to this mod's changes

analyzing-windows-prefetch-with-python is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 11d ago), licensed MIT. It adds 66 tokens to every session and 1,487 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to analyzing-windows-prefetch-with-python, differing in 9 lines, and is treated as a copy.

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