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.
55
45%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/analyzing-windows-prefetch-with-python/SKILL.mdQuality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong, specific description that clearly identifies its forensic analysis niche and lists concrete capabilities. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The domain-specific terminology serves as effective natural trigger terms for its target audience.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about Windows Prefetch files, .pf files, application execution forensics, or needs to analyze program execution artifacts from a Windows system.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Parse Windows Prefetch files', 'reconstruct application execution history', 'detect renamed or masquerading binaries', and 'identify suspicious program execution patterns'. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this dimension at 2 per the rubric guidelines. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords a forensic analyst would use: 'Windows Prefetch', 'prefetch files', 'application execution history', 'masquerading binaries', 'suspicious program execution', and the specific library name 'windowsprefetch'. These are terms users in DFIR would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche: Windows Prefetch file analysis is a very specific forensic domain with clear triggers (Prefetch, .pf files, execution history). Unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a high-level description of what a Prefetch analyzer should do, paired with a lengthy example output, but contains no actual executable code or concrete implementation guidance. Despite being explicitly about using the windowsprefetch Python library, it never shows a single line of Python code using that library. The verbose example output consumes most of the token budget while the actual instructional content remains abstract and vague.
Suggestions
Add complete, executable Python code showing how to use the windowsprefetch library to parse .pf files, extract run counts, timestamps, loaded DLLs, and volume information.
Replace the vague 4-step workflow with concrete implementation steps including actual commands/code, validation checks (e.g., verifying file format version, handling parse errors), and a feedback loop for corrupt files.
Remove the generic 'When to Use' section and trim the overview to essentials Claude doesn't already know (e.g., specific library API quirks, version-specific format differences).
Move the lengthy example output to a separate reference file and replace it with a concise inline example showing the key data structures returned by the windowsprefetch library.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'When to Use' section is generic boilerplate that adds no value. The overview explains what Prefetch files are (Claude already knows this). The steps are vague placeholders without any actual code. The massive example output consumes significant tokens while providing no executable guidance. | 1 / 3 |
Actionability | There is zero executable code in this skill despite being about Python-based analysis. Steps like 'Extract executable name, run count, last execution timestamps' and 'Flag known attack tools' describe what to do abstractly without any concrete implementation. No imports, no API calls, no actual Python code using the windowsprefetch library. | 1 / 3 |
Workflow Clarity | The four steps are vague descriptions ('Collect Prefetch Files', 'Parse Execution History', 'Detect Suspicious Execution', 'Build Execution Timeline') with no concrete commands, no validation checkpoints, and no error handling. There's no feedback loop for verifying parsing results or handling corrupt/unsupported prefetch files. | 1 / 3 |
Progressive Disclosure | The content has some structural organization with sections (Overview, When to Use, Prerequisites, Steps, Expected Output, Example Output), but the example output is excessively long and inline. There are no references to external files for detailed implementation, API reference, or detection rule lists that would benefit from separation. | 2 / 3 |
Total | 5 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
888bbe4
- Reviewed
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