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conducting-memory-forensics-with-volatility

Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

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-structured runbook: every workflow step is backed by executable Volatility 3 commands, detection guidance is concrete (specific IoCs, protection flags, parent-process expectations), and the scenario with pitfalls adds real judgment value. The two notable weaknesses are the disconnect between SKILL.md and the provided bundle files (no navigation to references/api-reference.md or scripts/agent.py) and the absence of explicit validation/error-recovery checkpoints in the workflow.

Suggestions

Add a short automation section pointing to the existing bundle so it is discoverable, e.g. "For automated bulk analysis, run scripts/agent.py (see references/api-reference.md for functions and CLI usage)" — this directly addresses the un-signaled references.

Trim the Key Concepts and Tools & Systems sections to only entries that aid interpretation of plugin output (e.g. keep VAD/EPROCESS and the PAGE_EXECUTE_READWRITE flag; drop generic definitions like process injection and one-line tool descriptions).

Insert an explicit validation checkpoint after Step 1 (recompute and compare the dump's SHA-256 before analysis begins) and a brief recovery path for the most common failure (missing symbol tables: "vol -f dump.raw windows.info fails → download symbols or set the symbol server") to add feedback loops to the workflow.

DimensionReasoningScore

Conciseness

The body is command-dense with tight inline commentary (e.g. "# Compare pslist vs psscan to find hidden processes") and no padded prose, but the Key Concepts table re-explains concepts Claude largely knows ("Process Injection | Technique where malware inserts code into a legitimate process's memory space...") and the Tools & Systems section re-describes well-known tools (YARA, Volatility). Minor trimming opportunities keep this below anchor 5.

4 / 5

Actionability

Commands are copy-paste ready and cover the common cases end-to-end: acquisition ("winpmem_mini_x64.exe output.raw", "./avml output.lime"), analysis ("vol -f memory.raw windows.pslist", "windows.malfind", "windows.yarascan --yara-file malware_rules.yar"), extraction ("windows.hashdump", "windows.dumpfiles --pid 3847"), and reporting. The only un-executed mention is CobaltStrikeParser in the scenario, which is an enrichment step rather than a gap in core guidance.

5 / 5

Workflow Clarity

Seven clearly sequenced steps (acquire → identify OS → processes → network → injection → credentials → report) with analytic cross-checks (pslist vs psscan for hidden processes, YARA confirmation of malfind findings, cross-referencing netscan with the process tree). Not anchor 5: there is no explicit post-acquisition integrity validation (verify the dump's SHA-256 before analysis) and no error-recovery feedback loops (e.g. what to do when symbol tables fail to resolve).

4 / 5

Progressive Disclosure

The body itself is well-sectioned (When to Use, Prerequisites, Workflow, Scenarios, Output Format), but the skill ships a bundle — references/api-reference.md and scripts/agent.py — that is never referenced or signaled anywhere in SKILL.md, leaving the automation agent and its API documentation undiscoverable. This matches anchor 3 ("references present but not clearly signaled") rather than anchor 4's "references mostly clear".

3 / 5

Total

16

/

20

Passed

Description

92%Weight 40%Scale 1-5

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 description that clearly states what the skill does, enumerates concrete capabilities across the full memory-forensics lifecycle, and provides an explicit activation clause with six natural trigger phrases. The only weakness is missing file-extension and a few natural phrasing variations in the trigger list. Third-person voice is used correctly.

DimensionReasoningScore

Specificity

Lists multiple concrete actions spanning the full workflow: "extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps" plus "Covers memory acquisition, process analysis, DLL inspection, and malware detection". This matches the comprehensive-coverage anchor; it exceeds anchor 4 because there are no significant gaps in the action inventory for this domain.

5 / 5

Completeness

Explicitly answers both questions: the what ("Performs memory forensics analysis using Volatility 3 to extract evidence of...") and the when ("Activates for requests involving memory forensics, RAM analysis...") with concrete trigger phrases. This is a direct match for the anchor-5 example structure; anchor 4 would require the when-clause to be less explicit.

5 / 5

Trigger Term Quality

"Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition" provides six natural trigger phrases with good synonym coverage (memory/RAM/volatile/dump). Falls short of anchor 5, which expects file extensions (.raw, .vmem, .dmp) and complete variation coverage (e.g. "analyze this memory image").

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (RAM analysis with Volatility) with distinct triggers ("memory forensics", "RAM analysis", "memory dump investigation", "volatile evidence analysis") that would not fire for disk forensics or general malware-analysis skills. Minimal conflict risk; anchor 4's "minor overlap risk with closely related skills" does not apply given the memory-specific trigger vocabulary.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

mukul975/Anthropic-Cybersecurity-Skills

Path

skills/conducting-memory-forensics-with-volatility/SKILL.md

Reviewed

Table of Contents

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