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.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

65%

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

A highly actionable, well-sequenced Volatility 3 workflow with copy-paste commands, but it is verbose for the context window and underuses its own bundle: the inline plugin reference duplicates references/api-reference.md, which is never linked, and explicit validation checkpoints are largely absent.

Suggestions

Replace the inline plugin command listings in Steps 2–5 with a pointer to references/api-reference.md, keeping only the workflow sequencing and decision logic in SKILL.md to remove redundancy and cut tokens.

Trim or remove the 'Key Concepts' and 'Tools & Systems' sections that restate concepts Claude already knows, and shorten the multi-page 'Output Format' sample report to the essential fields.

Add explicit validation checkpoints with a fix-and-retry loop — e.g. confirm symbols loaded and OS detection succeeded before running analysis plugins, and verify dumped-process hashes against the on-disk binary before concluding hollowing.

Dimension	Reasoning	Score
Conciseness	The ~280-line body restates concepts Claude already knows (the 'Key Concepts' and 'Tools & Systems' sections) and ships a long sample report, plus the inline plugin command listings duplicate references/api-reference.md; not 1 because the bulk is actionable commands rather than conceptual prose, not 3 because of the redundancy and explanatory padding that could be tightened.	2 / 3
Actionability	Provides many concrete, copy-paste-ready Volatility 3 commands with flags and expected outputs (e.g. 'vol3 -f memory.dmp windows.malfind --dump --pid 2184'); not 2 because the code is fully executable rather than pseudocode or abstract guidance.	3 / 3
Workflow Clarity	A clear seven-step sequence exists, but validation checkpoints are mostly implicit (only the pslist-vs-psscan comparison is a real check) with no fix-and-retry feedback loop; not 1 because the steps are well sequenced, capped at 2 by the missing explicit validation/verification steps.	2 / 3
Progressive Disclosure	Bundle files references/api-reference.md and scripts/agent.py exist but are never referenced from the body, and plugin reference content that belongs in api-reference.md is inlined in SKILL.md; not 1 because the body is organized into clear sections rather than a monolithic wall, not 3 because the reference is unsignaled and content that should be separate is inline.	2 / 3
	Total	9 / 12 Passed

Description

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.

A strong, third-person description that concisely states concrete capabilities and provides explicit, well-chosen trigger terms covering the memory-forensics domain. It clearly answers both 'what' and 'when' with minimal padding.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'identify malicious processes, injected code, network connections, loaded modules, and extracted credentials' — matching the 'lists multiple specific concrete actions' anchor; not 2 because the actions are comprehensive rather than a partial set.	3 / 3
Completeness	Explicitly states what it does ('Analyzes RAM memory dumps... to identify...') and when to trigger ('Activates for requests involving memory forensics, RAM analysis...'), satisfying both halves with an explicit trigger clause; not 2 because the 'when' guidance is explicit rather than implied.	3 / 3
Trigger Term Quality	Covers natural terms a DFIR user would say — 'memory forensics', 'RAM analysis', 'process injection detection' — plus reasonable variations; not 2 because coverage is broad rather than missing common variations.	3 / 3
Distinctiveness Conflict Risk	Scoped to a clear niche (Volatility-based RAM forensics) with distinct triggers unlikely to fire for unrelated skills; not 2 because the domain and triggers are specific enough to avoid overlap with general forensic or malware skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: mukul975/Anthropic-Cybersecurity-Skills
Commit: 673da1f

Reviewed: 13 days ago

Table of Contents

Discovery Implementation Validation

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