analyzing-linux-elf-malware
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.
68
82%
Does it follow best practices?
Impact
—
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 and platforms, and provides explicit activation triggers. It uses proper third-person voice throughout and would be easily distinguishable from other skills in a large skill library.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and domains: 'static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples' along with specific malware types (botnets, cryptominers, ransomware, rootkits) and targets (Linux servers, containers, cloud infrastructure). | 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'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'Linux malware', 'ELF binary', 'malware analysis', 'reverse engineering', 'ransomware', 'rootkits', 'cryptominers', 'botnets', 'container malware', 'Linux server compromise'. These are terms a security analyst would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused 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, 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 with executable commands and code throughout all workflow steps. Its main weaknesses are verbosity in supplementary sections (Key Concepts, Tools & Systems) that explain things Claude already knows, and the lack of explicit validation checkpoints and safety gates in a workflow that involves executing malware. The monolithic structure could benefit from splitting reference material into separate files.
Suggestions
Add explicit validation/safety checkpoints between steps — e.g., verify architecture match before dynamic execution, confirm sandbox isolation before running strace, validate UPX unpacking succeeded before proceeding to string analysis.
Remove or significantly trim the Key Concepts table and Tools & Systems section, as Claude already knows what ELF, strace, GDB, and Ghidra are — keep only non-obvious details like the LD_PRELOAD abuse pattern.
Add a clear safety warning section at the top with a pre-execution checklist (VM snapshot taken, network isolated, architecture verified) rather than burying safety notes in parentheticals within steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary content like the Key Concepts table defining terms Claude already knows (ELF, strace, stripped binary). The Tools & Systems section also restates information already evident from the workflow. However, the workflow steps themselves are reasonably efficient with executable commands. | 2 / 3 |
Actionability | The skill provides fully executable bash commands and Python code throughout. Every step includes copy-paste ready commands (readelf, strace, GDB breakpoints, grep patterns for IOCs). The Python ELF analysis script with pyelftools is complete and runnable. | 3 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced and logically ordered from triage through deep analysis. However, there are no explicit validation checkpoints or feedback loops between steps — for example, no verification that unpacking succeeded before proceeding, no check that the architecture matches 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 with no references to external files for detailed content. The Key Concepts table, Tools & Systems section, and detailed scenario could be split into separate reference files. However, the section structure is clear and well-organized with logical headers, which partially compensates. | 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 | |
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
0f429d0
- Reviewed
Table of Contents
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