CC performing-memory-forensics-with-volatility3
Analyze volatile memory (RAM) dumps using the Volatility 3 framework to extract running processes, network connections, loaded modules, credentials, and encryption keys, and to detect process hollowing, DLL injection, or hidden processes/rootkits. Use during incident response on a compromised or suspect system when disk-based forensics alone is insufficient and volatile evidence of malware or intrusion must be recovered from memory.
Analyze volatile memory (RAM) dumps using the Volatility 3 framework to extract running processes, network connections, loaded modules, credentials, and…
As a process C 60/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice
How to improve
- 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
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low Risky intent
intent-offensive-securitySKILL.md:269Offensive-security / dual-use content (legitimate for authorised testing; review intended use)Run hashdump and lsadump to extract cached credentials, identify mimikatz execution in cmdline output, check for lsass.exe memory dumps in filesystem artifacts, correlate with lateral movement evidenc
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low Risky intent
intent-offensive-securitySKILL.md:274Offensive-security / dual-use content (legitimate for authorised testing; review intended use)**Scenario 4: Ransomware Incident Recovery**
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low Risky intent
intent-offensive-securitySKILL.md:275Offensive-security / dual-use content (legitimate for authorised testing; review intended use)Extract encryption keys from ransomware process memory before system shutdown, identify the ransomware variant using YARA, find the initial execution point through command line artifacts, map lateral
A further 1 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "domain" - note
frontmatter-keyunknown frontmatter key "subdomain" - note
frontmatter-keyunknown frontmatter key "nist_csf" - note
frontmatter-keyunknown frontmatter key "mitre_attack"
Process rating: all ten parameters 60/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 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. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2798 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
- +4Description does not say when NOT to use the skill (false activations)
- -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
- +3Description length 436: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 11 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.