analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.
90
88%
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 articulates specific capabilities (analyzing RAM dumps, identifying malicious processes, injected code, network connections, etc.), names the specific tool (Volatility framework), and provides explicit activation triggers. It covers both 'what' and 'when' comprehensively, uses natural domain-appropriate terminology, and occupies a clearly distinct niche in memory forensics.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'identify malicious processes, injected code, network connections, loaded modules, and extracted credentials' along with the specific tool (Volatility framework) and supported platforms (Windows, Linux, macOS). | 3 / 3 |
Completeness | Clearly answers both 'what' (analyzes RAM memory dumps to identify malicious processes, injected code, network connections, etc.) and 'when' ('Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'memory dumps', 'memory forensics', 'RAM analysis', 'volatile data examination', 'process injection detection', 'memory-resident malware', 'compromised systems', 'Volatility framework'. These are terms a forensics analyst would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — memory forensics with the Volatility framework is a very specific domain unlikely to conflict with other skills. The trigger terms are domain-specific and clearly scoped to RAM/memory analysis of compromised systems. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable memory forensics skill with excellent workflow sequencing and concrete, executable commands. Its main weaknesses are moderate verbosity from glossary/tool sections that explain concepts Claude already knows, and a monolithic structure that could benefit from splitting reference material into separate files. The suspicious process indicators checklist, detailed scenario, and comprehensive output format template are particularly valuable additions.
Suggestions
Remove or significantly trim the 'Key Concepts' glossary table and 'Tools & Systems' section — Claude already knows these concepts and tools; at most, keep a one-line note about less obvious terms like VAD or EPROCESS.
Extract the output format template and the suspicious process indicators checklist into separate reference files (e.g., REPORT_TEMPLATE.md, INDICATORS.md) and link to them from the main skill to reduce token footprint.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary content. The 'Key Concepts' glossary table explains terms like 'Memory Forensics' and 'Fileless Malware' that Claude already knows. The 'Tools & Systems' section describes tools at a level Claude doesn't need. However, the command examples themselves are lean and useful. | 2 / 3 |
Actionability | Excellent actionability with fully executable bash commands throughout, specific Volatility 3 plugin names, concrete flags and arguments, and real-world examples including YARA rule syntax. Commands are copy-paste ready with clear explanations of what each produces. | 3 / 3 |
Workflow Clarity | The 7-step workflow is clearly sequenced from profile identification through timeline generation. It includes validation checkpoints like comparing pslist vs psscan to detect hidden processes, cross-referencing PIDs with process lists, and the scenario section includes a feedback-loop approach. The 'Pitfalls' section adds error-awareness. | 3 / 3 |
Progressive Disclosure | The content is a monolithic document at ~250 lines with no references to external files for detailed content. The Key Concepts table, Tools & Systems section, and detailed output format template could be split into separate reference files. However, the section headers provide reasonable internal navigation. | 2 / 3 |
Total | 10 / 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
Table of Contents
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