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analyzing-memory-forensics-with-lime-and-volatility

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

74

Quality

68%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/analyzing-memory-forensics-with-lime-and-volatility/SKILL.md
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 a strong skill description that clearly specifies the tools (LiME, Volatility 3), concrete actions (extracting process lists, network connections, bash history, kernel modules, injected code), and an explicit 'Use when' trigger clause for incident response on compromised Linux systems. It is highly distinctive and uses natural terminology that forensics practitioners would use.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: memory acquisition using LiME, analysis with Volatility 3, extracting process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images.

3 / 3

Completeness

Clearly answers both what (performs memory acquisition with LiME and analysis with Volatility 3, extracting specific artifacts) and when ('Use when performing incident response on compromised Linux systems') with an explicit trigger clause.

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'memory acquisition', 'LiME', 'Volatility 3', 'incident response', 'compromised Linux systems', 'process lists', 'network connections', 'bash history', 'kernel modules', 'injected code', 'memory images'.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche combining Linux memory forensics, LiME, and Volatility 3 — very unlikely to conflict with other skills. The specific tooling and domain (incident response, memory analysis) create a clear, unique identity.

3 / 3

Total

12

/

12

Passed

Implementation

37%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill provides a reasonable starting point for Linux memory forensics with LiME and Volatility 3, including real commands and a basic workflow. However, it is weakened by generic boilerplate sections, incomplete Python code, lack of validation/verification steps critical for forensic workflows, and no progressive disclosure to deeper reference materials. The workflow is particularly lacking for a forensic context where evidence integrity verification is essential.

Suggestions

Add explicit validation checkpoints: verify LiME module loaded successfully (dmesg | grep lime), verify dump file size/integrity (sha256sum, file size check), and validate Volatility can parse the image before running all plugins.

Remove the generic 'When to Use' and 'Prerequisites' boilerplate sections—Claude doesn't need to be told about 'appropriate authorization' or 'familiarity with security operations concepts.'

Complete the Python example with actual execution and output handling, or remove it in favor of the more actionable CLI commands.

Add a step for building the LiME kernel module for the target system (make -C /lib/modules/$(uname -r)/build M=$(pwd) modules) since this is a critical prerequisite that's non-obvious.

DimensionReasoningScore

Conciseness

The 'When to Use' and 'Prerequisites' sections contain generic filler (e.g., 'Familiarity with security operations concepts,' 'Appropriate authorization for any testing activities') that Claude already knows and doesn't need. The core technical content is reasonably lean but surrounded by boilerplate.

2 / 3

Actionability

Provides real commands (insmod, vol3) and a Python snippet, but the Python code is incomplete (no actual execution or output handling), and the LiME command assumes a pre-built module without explaining how to build it. The commands are mostly copy-paste ready but missing key details like building the LiME kernel module for the target system.

2 / 3

Workflow Clarity

The numbered steps list actions but lack any validation checkpoints or error recovery. Memory acquisition is a destructive/one-shot operation where verification is critical (e.g., verifying the dump integrity, checking file size, validating the image before analysis). There are no feedback loops or verification steps, which should cap this at 2 at most, and the steps themselves are vague enough to warrant a 1.

1 / 3

Progressive Disclosure

Content is organized into sections (When to Use, Prerequisites, Instructions, Examples) which provides some structure, but everything is inline in a single file with no references to deeper materials. The Examples section largely duplicates the Instructions section commands. For a skill of this complexity, separating detailed plugin references or advanced analysis techniques into linked files would improve navigation.

2 / 3

Total

7

/

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

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