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CC performing-memory-forensics-with-volatility3-plugins

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 8 files · 2 scripts body ≈ 1 948 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerSecuritySoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
96
Quality 40%
75
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Risky intent intent-offensive-security references/standards.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Key Plugins for Malware Analysis
    • low Risky intent intent-offensive-security SKILL.md:41
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      Volatility3 (v2.26.0+, feature parity release May 2025) is the standard framework for memory forensics, replacing the deprecated Vola…y2. It analyzes RAM dumps from Windows, Linux, and macOS to de
      detector
    • low Risky intent intent-offensive-security SKILL.md:65
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      """Vola…sed memory forensics automation for malware analysis."""
      quoted
    • low Risky intent intent-offensive-security SKILL.md:73
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      """Automate Volatility3 plugin execution for malware analysis."""
      quoted

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "d3fend_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"
    • note edit-residue the text marks something as outdated (lines 5): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1948 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 165: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.