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.
npx skills add xalgorix/xalgorix --skill analyzing-prefetch-files-for-execution-historygit clone --depth 1 https://github.com/xalgorix/xalgorixWrote 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.
[](https://agentmods.dev/skills/xalgorix/xalgorix/analyzing-prefetch-files-for-execution-history)<a href="https://agentmods.dev/skills/xalgorix/xalgorix/analyzing-prefetch-files-for-execution-history"><img src="https://agentmods.dev/badge/skills/xalgorix/xalgorix/analyzing-prefetch-files-for-execution-history/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xalgorix/xalgorix/analyzing-prefetch-files-for-execution-history"><img src="https://agentmods.dev/badge/skills/xalgorix/xalgorix/analyzing-prefetch-files-for-execution-history.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 194 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- high YARA Match · line 194 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.03606 |
| Opus 5 | $0.00017 | $0.01803 |
| Sonnet 5 | $0.00007 | $0.00721 |
| Haiku 4.5 | $0.00003 | $0.00361 |
Grade A, and why
analyzing-prefetch-files-for-execution-history 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 8d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
Detection Gaps & Validation
- Prefetch disabled or absent != no execution: Prefetch is off by default on Windows Server, and on SSD systems
EnablePrefetcheris often set to 0. An empty or sparseC:\Windows\Prefetch\does not mean nothing ran - confirm the policy atHKLM\SYSTEM\CurrentControlSet\Control\Session Manager\Memory Management\PrefetchParametersbefore concluding "no execution," then pivot to Amcache/ShimCache/SRUM/Event Logs. - Most-missed details inside the .pf: parse the up-to-8 last-run timestamps (not just the most recent), the run count, and the referenced-file list (loaded DLLs, opened data files). The embedded path hash matters: a hash that doesn't match the on-disk path means the binary ran from a different location (USB, deleted folder) than where it now sits.
- Anti-forensics that defeats this analysis: attackers delete individual
.pffiles or clear the folder - a present executable with no Prefetch, or a folder with fewer files than an active system accumulates, is itself suspicious. Recover deleted.pffrom$MFT/unallocated and Volume Shadow Copies, and corroborate the gap with USN delete events. - Validate with a second source: Prefetch proves a binary executed at least once - confirm the what/when against Amcache (
Amcache.hve), ShimCache (AppCompatCache), SRUM, Security 4688 / Sysmon 1 process-creation events, and the on-disk file's$MFTtimes before attributing a run to a user or time. - Interpretation pitfalls (false positives): the last-run time is when the prefetch was written (~10s after launch start, with historical caveats), run count can reset, and renamed malware (e.g.
svchost.exefrom%TEMP%) hides behind a trusted name - check the path hash and referenced files, not the filename. Confirm system timezone and clock skew.
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.
- 8d ago First seen · 332 lines · 34 tokens per session scan A 7d521b76ebc3
analyzing-prefetch-files-for-execution-history is a skill published in the GitHub repository xalgorix/xalgorix (975 stars, last pushed 2d ago), licensed Apache-2.0. It adds 34 tokens to every session and 3,606 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
analyzing-prefetch-files-for-execution-history
Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
analyzing-prefetch-files-for-execution-history
Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
analyzing-prefetch-files-for-execution-history
Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
analyzing-prefetch-files-for-execution-history
A guide for reading Windows Prefetch files, which record information about programs that have run on a system. The records can include execution counts, times, and referenced files.
analyzing-prefetch-files-for-execution-history
Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
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 Windows system, confirming whether a…