CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

72

Quality

88%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Risky

Do not use without reviewing

SKILL.md
Quality
Evals
Security

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 for malicious artifacts), names the tool and supported platforms, and provides explicit activation triggers. It uses proper third-person voice throughout and covers natural trigger terms comprehensively, making it easy for Claude to select this skill precisely when needed.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'identify malicious processes, injected code, network connections, loaded modules, and extracted credentials.' Also names 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, etc.) and 'when' with explicit triggers ('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 forensics', 'RAM analysis', 'volatile data examination', 'process injection detection', 'memory-resident malware', 'memory dumps', 'compromised systems'. 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 general malware analysis, disk forensics, or other security skills due to the explicit focus on RAM/memory dumps.

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 structure and real, executable commands throughout. Its main weaknesses are moderate verbosity — particularly the glossary table, tools descriptions, and some explanatory comments that Claude doesn't need — and the monolithic structure that could benefit from splitting reference material into separate files. The detailed output format template and scenario walkthrough are valuable additions that enhance practical utility.

Suggestions

Remove or significantly trim the Key Concepts table and Tools & Systems section — Claude already knows what process hollowing, fileless malware, and Volatility are.

Extract the output format template and suspicious process indicators into separate reference files (e.g., REPORT_TEMPLATE.md, INDICATORS.md) to reduce the main skill's token footprint and improve progressive disclosure.

DimensionReasoningScore

Conciseness

The skill is reasonably efficient but includes unnecessary content: the Key Concepts table explains terms Claude already knows (e.g., what fileless malware is, what process hollowing is), the Tools & Systems section describes well-known tools, and some inline comments are redundant. The 'When to Use' and 'Prerequisites' sections add moderate value but could be tighter.

2 / 3

Actionability

Excellent actionability throughout — every step provides specific, executable Volatility 3 commands with real flags and arguments. The commands are copy-paste ready, include filtering examples (grep patterns), and cover the full forensic workflow from identification through reporting. The suspicious process indicators checklist is highly practical.

3 / 3

Workflow Clarity

The 7-step workflow is clearly sequenced in logical forensic order (identify → enumerate → detect injection → network → extract → YARA scan → timeline). Validation checkpoints are present: comparing pslist vs psscan to find hidden processes, verifying dumped EXEs against disk, and the scenario section includes a feedback-oriented approach. The cross-referencing guidance (e.g., cross-reference PIDs with process list) serves as implicit validation.

3 / 3

Progressive Disclosure

The content is a monolithic document with no references to external files for advanced topics. The Key Concepts table, Tools & Systems section, and detailed output format template could be split into separate reference files. However, the internal structure with clear headers and sections provides reasonable navigability within the single file.

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
mukul975/Anthropic-Cybersecurity-Skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.