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 oyi77/1ai-skills --skill analyzing-windows-prefetch-with-pythongit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/analyzing-windows-prefetch-with-python)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-windows-prefetch-with-python"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-windows-prefetch-with-python/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/oyi77/1ai-skills/analyzing-windows-prefetch-with-python"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-windows-prefetch-with-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 77 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.
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 | $0.00056 | $0.01872 |
| Opus 5.5 | $0.00022 | $0.00749 |
| Sonnet 5.5 | $0.00011 | $0.00374 |
| Haiku 4.5 | $0.00006 | $0.00187 |
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 1mo 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 — 162 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
Trigger phrases:
-
"analyzing windows prefetch with python"
-
"Parse Windows Prefetch files using the windowsprefetch Python library to reconst"
-
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
When NOT to Use
- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope
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
- Scope the task — define objectives, boundaries, and success criteria
- Gather information — collect all necessary data and context before proceeding
- Execute the core workflow — follow the domain-specific steps methodically
- Validate results — verify outputs against expected outcomes or baselines
- Document findings — record results, anomalies, and recommendations
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.
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.
- 1mo ago First seen · 162 lines · 56 tokens per session scan A 7122a9070202
analyzing-windows-prefetch-with-python is a skill published in the GitHub repository oyi77/1ai-skills (16 stars, last pushed 2d ago), licensed MIT. It adds 56 tokens to every session and 1,872 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-04.
Other skills, from other repositories
analyzing-windows-prefetch-with-python
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.
analyzing-windows-prefetch-with-python
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.
analyzing-windows-prefetch-with-python
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…
analyzing-windows-prefetch-with-python
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.
analyzing-windows-prefetch-with-python
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.
analyzing-windows-prefetch-with-python
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.