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analyzing-slack-space-and-file-system-artifacts

Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.

72

Quality

90%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Critical

Do not install without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

80%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with complete executable code and a clear five-step workflow, but it is verbose, inlines material that duplicates the provided reference bundle without linking to it, and lacks validation checkpoints for forensic operations on evidence.

Suggestions

Move the full USN_RECORD_V2 parser and TSK/MFTECmd command reference into references/api-reference.md and link to it from the body to reduce inline verbosity and avoid duplication.

Add explicit validation/verification checkpoints between steps (e.g., verify MFT extraction succeeded before parsing, confirm record counts look sane) to satisfy the feedback-loop requirement for forensic batch operations.

Reference scripts/agent.py from the body and trim inline explanatory comments (RAM slack, Zone.Identifier content) that Claude already knows.

DimensionReasoningScore

Conciseness

The body is very long (~370 lines) with full inline parsers and explanatory comments such as 'RAM slack: bytes between file end and next sector boundary' and the Zone.Identifier content reveal; it largely duplicates content already in references/api-reference.md, so it could be tightened significantly.

2 / 3

Actionability

Provides concrete, copy-paste-ready TSK commands and complete executable Python scripts (MFT parsing, USN_RECORD_V2 parsing, ADS extraction) rather than vague direction or pseudocode.

3 / 3

Workflow Clarity

Five steps are clearly sequenced, but operations on an evidence image (extraction, carving, parsing) have no validation/verification checkpoints or error-recovery feedback loops, which caps workflow clarity at 2 for batch/forensic operations.

2 / 3

Progressive Disclosure

Bundle files exist (references/api-reference.md, scripts/agent.py) but the body never signals or links to them, and large code/API content that belongs in those references is inlined instead, so structure could be better organized.

2 / 3

Total

9

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, uses third person, includes natural trigger terms, and clearly states both capabilities and when to invoke the skill. It is concise yet comprehensive and unlikely to conflict with other skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS)', 'recover hidden or residual data, reconstruct deleted-file metadata' — rather than vague language.

3 / 3

Completeness

Explicitly answers both what (examine/recover/reconstruct) and when via 'Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.'

3 / 3

Trigger Term Quality

Covers natural domain terms a forensic analyst would say — 'NTFS slack space', 'MFT', 'USN Change Journal', 'Alternate Data Streams (ADS)', 'NTFS image', 'standard file recovery' — including the common acronym ADS.

3 / 3

Distinctiveness Conflict Risk

A clearly narrow NTFS forensic-artifact niche with distinct triggers that would not plausibly fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation15 / 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
Reviewed

Table of Contents

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