Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.
85
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Risky
Do not use without reviewing
Quality
Discovery
100%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 an excellent skill description that clearly defines its scope (Linux ELF malware analysis), lists specific capabilities (static analysis, dynamic tracing, reverse engineering), enumerates target malware types (botnets, cryptominers, ransomware, rootkits), and provides explicit activation triggers. It uses proper third-person voice throughout and covers a well-defined niche that would be easily distinguishable from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and targets: 'Analyzes malicious Linux ELF binaries including botnets, cryptominers, ransomware, and rootkits' and 'Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples.' These are concrete, specific capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('Analyzes malicious Linux ELF binaries... Covers static analysis, dynamic tracing, and reverse engineering') and when ('Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis'). The 'Activates for' clause serves as an explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms: 'Linux malware', 'ELF binary', 'botnets', 'cryptominers', 'ransomware', 'rootkits', 'Linux servers', 'containers', 'cloud infrastructure', 'reverse engineering', 'x86_64', 'ARM', 'container malware analysis', 'Linux server compromise'. These are terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focusing specifically on Linux ELF malware analysis. The combination of Linux-specific, ELF-specific, and malware-specific terms makes it very unlikely to conflict with general code analysis, Windows malware analysis, or other security skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, highly actionable skill for Linux ELF malware analysis with excellent executable examples and comprehensive coverage of the analysis workflow. Its main weaknesses are verbosity in definitional sections (Key Concepts, Tools & Systems) that Claude doesn't need, and the lack of explicit validation checkpoints and safety gates in a workflow that involves executing malicious code. The monolithic structure could benefit from splitting reference material into separate files.
Suggestions
Remove or drastically reduce the Key Concepts table and Tools & Systems section — Claude already knows what ELF, strace, GDB, and Mirai are. Keep only project-specific or non-obvious information.
Add explicit safety validation checkpoints: verify architecture match before dynamic execution, confirm sandbox isolation before running strace/GDB, validate UPX unpacking success before proceeding to string analysis.
Split the Common Scenarios, Output Format, and reference tables into separate linked files (e.g., SCENARIOS.md, REPORT_TEMPLATE.md) to reduce the main skill's token footprint.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary content like the Key Concepts table defining terms Claude already knows (e.g., what ELF stands for, what strace is, what Mirai is). The Tools & Systems section also restates information already evident from usage in the workflow. However, the workflow steps themselves are reasonably efficient. | 2 / 3 |
Actionability | Excellent actionability with fully executable bash commands and Python code throughout. Every step includes copy-paste ready commands (readelf, strace, GDB breakpoints, grep patterns). The Python ELF analysis script is complete and executable with pyelftools. | 3 / 3 |
Workflow Clarity | The six steps are clearly sequenced and logically ordered from triage through deep analysis. However, there are no explicit validation checkpoints or feedback loops — for example, no verification that UPX unpacking succeeded before proceeding, no check that the binary matches the expected architecture before dynamic analysis, and no explicit safety gates before executing malware in Step 3/4 beyond parenthetical notes. | 2 / 3 |
Progressive Disclosure | The content is a monolithic document at ~250 lines with no references to external files for detailed topics. The Key Concepts table, Tools & Systems section, and detailed scenario could be split into separate reference files. The structure within the file is good with clear headers, but everything is inline. | 2 / 3 |
Total | 9 / 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 | |
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Table of Contents
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