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collecting-volatile-evidence-from-compromised-host

Collect volatile forensic evidence from a compromised host by following the order of volatility, preserving memory, network connections, running processes, and system state with documented chain of custody before they are lost. Use before isolating, shutting down, or remediating a compromised host, especially when fileless or memory-resident malware is suspected, root cause analysis is needed, or the evidence must hold up in legal proceedings.

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

75%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.

The body is a highly actionable, well-sequenced live-collection runbook with strong per-OS command coverage and integrity checkpoints. Its main weakness is structure across files: none of the bundle's references, scripts, or the report template are referenced anywhere, so the detailed workflow material and automation the bundle provides cannot be found or used.

Suggestions

Add a 'References' or 'Bundle files' section near the top pointing to references/workflows.md (platform checklists and common pitfalls), references/standards.md (RFC 3227 / NIST mapping), references/api-reference.md, and assets/template.md (evidence report template).

Surface the automation path: mention scripts/process.py and scripts/agent.py (which already implement the ordered collection and hashing) as the preferred route, keeping the inline manual commands as a fallback for restricted hosts.

Add explicit validation feedback loops: instruct the responder to abort collection if the Step 1 toolkit hash diff fails, and to verify evidence hashes before and after any transfer (per the hashing protocol in references/workflows.md).

DimensionReasoningScore

Conciseness

The body is dominated by command blocks with no filler and every section earns its place, but the Key Concepts table briefly re-explains well-known principles (Locard's Exchange Principle, chain of custody) and some capture sets are redundant (netstat alongside ss and /proc/net/tcp) without stating the redundancy is intentional.

4 / 5

Actionability

Every step provides concrete, copy-paste-ready commands with specific output paths, per-OS coverage (Windows/Linux/macOS), and an evidence manifest plus chain-of-custody record generated with real shell code — the common cases are fully covered.

5 / 5

Workflow Clarity

The seven-step workflow follows a clear volatility-ordered sequence and includes real checkpoints (toolkit hash verification in Step 1, evidence hashing and custody documentation in Step 7). It falls short of a 5 because there are no feedback loops — e.g., what to do if the toolkit hash diff fails or a memory dump errors out — though checkpoints are present, keeping it above a 3.

4 / 5

Progressive Disclosure

The body never mentions any bundle file: references/workflows.md (platform checklists and pitfalls), references/standards.md, references/api-reference.md, assets/template.md, and two ready-made collection scripts (scripts/process.py, scripts/agent.py) are all undiscoverable from SKILL.md, while ~170 lines of inlined platform commands duplicate workflows.md content — references are effectively buried.

2 / 5

Total

15

/

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: specific, third-person, with explicit 'Use when' trigger guidance covering fileless malware, root cause analysis, and legal hold-up requirements. Trigger keyword coverage is good but could add common synonyms such as 'incident response', 'live forensics', or 'memory dump' to broaden natural-match surface.

DimensionReasoningScore

Specificity

Enumerates multiple concrete actions — 'preserving memory, network connections, running processes, and system state with documented chain of custody' — grounded in the 'order of volatility' methodology; coverage of the niche is comprehensive, so it exceeds the 'minor gaps' anchor.

5 / 5

Completeness

Explicitly answers both questions: the 'what' is a concrete third-person capability statement, and the 'when' is an explicit 'Use before isolating, shutting down, or remediating... especially when...' clause with multiple concrete trigger conditions.

5 / 5

Trigger Term Quality

Natural incident-response phrases like 'compromised host', 'fileless or memory-resident malware', 'root cause analysis', and 'legal proceedings' are present, but common variations such as 'incident response', 'live forensics', 'memory dump', or 'DFIR' are absent, leaving a few natural terms missing.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche — live volatile evidence collection prior to remediation — with triggers (pre-shutdown collection, fileless malware, court-admissible evidence) that are distinct from general incident-response or malware-analysis skills.

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/collecting-volatile-evidence-from-compromised-host/SKILL.md

Reviewed

Table of Contents

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