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CC 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.

Not recommendedcritical or high security findings
mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 732 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded…

As a process C 63/100 · Has gaps — weak spots: failures and branches, running it twice, progress reporting

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
79/100
safety, quality, tests
Safety 60%
79
Quality 40%
78
Run on models
none yet
Process rating
C
63/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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

  • high Dangerous commands cmd-encoded-exec SKILL.md:301
    Executes a base64/encoded payload (documentation of a security skill)
    PID 4012: powershell.exe -enc JABj…0AA==
    security skill
Medium and low: 3
  • low Risky intent intent-offensive-security SKILL.md:252
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    **Context**: EDR detected suspicious PowerShell activity but the threat actor cleaned up disk artifacts. A memory dump was captured before the system was rebooted. The analysis needs to identify the m
  • low Risky intent intent-offensive-security SKILL.md:261
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    7. Extract credentials with `hashdump` and `lsadump` to assess lateral movement risk

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 4. 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 "mitre_attack"
  • note frontmatter-key unknown frontmatter key "nist_csf"
  • note edit-residue the text marks something as outdated (lines 39): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 63/100

  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2732 tokens

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 409: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 27 items
  • +3Output format is stated explicitly
  • +4Has examples (9 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.