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.
44
45%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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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 the domain (Windows Prefetch forensic analysis), the tool (windowsprefetch Python library), and concrete actions (parsing, reconstructing execution history, detecting masquerading). Its main weakness is the absence of an explicit 'Use when...' clause that would help Claude know exactly when to select this skill.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about Windows Prefetch files, .pf files, application execution artifacts, or forensic timeline analysis.'
| 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 actions, but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this dimension at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords a user would say: 'Windows Prefetch', 'prefetch files', 'application execution history', 'renamed binaries', 'masquerading', 'suspicious program execution', and mentions the specific Python library 'windowsprefetch'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focusing specifically on Windows Prefetch file analysis using a named Python library; very unlikely to conflict with other skills due to the narrow forensic/DFIR domain. | 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 fails at its core purpose: teaching Claude how to parse Windows Prefetch files using the windowsprefetch Python library. It contains no executable Python code, no API usage examples, and no concrete implementation guidance despite being explicitly about using a specific library. The content reads like a high-level requirements document rather than an actionable skill, with verbose boilerplate sections and a lengthy hypothetical CLI output example that references a tool never defined or provided.
Suggestions
Replace the abstract steps with actual executable Python code using the windowsprefetch library (e.g., `import windowsprefetch; pf = windowsprefetch.Prefetch('EXECUTABLE-HASH.pf'); print(pf.executableName, pf.runCount, pf.lastRunTime)`)
Remove the generic 'When to Use' boilerplate and the explanation of what Prefetch files are - Claude already knows this. Focus tokens on library-specific API details and detection logic.
Add concrete code for the renamed binary detection logic (comparing executable names against loaded DLL patterns) and suspicious tool detection (specific string matching or heuristics).
Add validation steps such as checking file format version compatibility, handling corrupt/truncated Prefetch files, and verifying parsed output integrity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is verbose with unnecessary filler ('When to Use' section is generic boilerplate, the overview explains what Prefetch files are which Claude already knows). The steps section is vague padding without actionable content. The example output, while illustrative, is extremely long and could be condensed significantly. | 1 / 3 |
Actionability | There is zero executable code despite the skill being about using a Python library. The steps are abstract descriptions ('Gather .pf files', 'Extract executable name') with no actual Python code, no API usage examples, no concrete commands. The example output shows a hypothetical CLI tool that doesn't exist and isn't provided. | 1 / 3 |
Workflow Clarity | The four steps are vague descriptions without concrete implementation details, validation checkpoints, or error handling. There's no feedback loop for handling corrupt Prefetch files, no verification of parsing results, and no actual sequence of executable operations. | 1 / 3 |
Progressive Disclosure | The content is organized into logical sections (Overview, Prerequisites, Steps, Expected Output) which provides some structure. However, there are no bundle files or references to supporting materials, and the lengthy example output is inline when it could be separated. No deeply nested references though. | 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
9a588e6
- Reviewed
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