analyzing-malware-behavior-with-cuckoo-sandbox

Detonate malware samples in Cuckoo Sandbox to observe runtime behavior — process creation, file system and registry changes, network communications, and API calls — and generate behavioral reports for classification and IOC extraction. Use when a sample has passed static triage and needs dynamic/behavioral analysis, when mapping a full infection chain, or when building YARA/behavioral signatures from observed sandbox activity.

Quality

76%

Does it follow best practices?

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Securityby

Low

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tessl review fix ./skills/analyzing-malware-behavior-with-cuckoo-sandbox/SKILL.md

Quality

Content

61%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.

Highly actionable with executable code throughout and a clear 7-step sequence, but weakened by redundant concept explanations, inlined API-reference content that duplicates an existing bundle file, and no navigation to the provided references/scripts. Missing validation checkpoints cap workflow clarity for this destructive operation.

Suggestions

Replace the inlined curl/REST/report-JSON material in Steps 1-7 with a brief summary and a pointer to references/api-reference.md to remove duplication and enable progressive disclosure.

Reference scripts/agent.py (e.g. under an 'Automated/bulk analysis' section) so the provided automation script is discoverable instead of orphaned.

Add explicit validation checkpoints to the workflow — verify network isolation before submit, confirm task status is 'reported' before parsing report.json, and a fix/re-submit loop on failure.

Trim the 'Key Concepts' and 'Tools & Systems' sections to malware-analysis-specific nuance, removing definitions of well-known concepts (Dynamic Analysis, Process Injection) and the duplicate InetSim definition.

Dimension	Reasoning	Score
Conciseness	Mostly actionable code, but the 'Key Concepts' and 'Tools & Systems' sections explain concepts Claude already knows (e.g. Dynamic Analysis, Process Injection) and define InetSim twice, and the body inlines API-reference material that already exists in references/api-reference.md.	3 / 5
Actionability	Provides copy-paste-ready bash (cuckoo submit, curl, vol3) and complete executable Python for parsing report.json across process, network, file, registry, and signature analysis, covering the common cases.	5 / 5
Workflow Clarity	Seven steps are clearly sequenced, but this destructive/batch operation (malware detonation) lacks explicit validation checkpoints — no 'verify task status == reported before parsing' or 'verify network isolation before submit' feedback loop — capping it at 3 per the rubric.	3 / 5
Progressive Disclosure	Bundle files references/api-reference.md and scripts/agent.py exist but are never referenced from the body, while the API-reference content that belongs in the separate file is inlined in SKILL.md — content that should be separate is inlined and references are absent.	2 / 5
	Total	13 / 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, third-person description that concretely states capabilities and provides explicit, multi-condition 'Use when' triggers with minimal conflict risk. Only slight keyword-comprehensiveness gaps (no file extensions/synonyms) keep trigger_term_quality at 4.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Detonate malware samples', 'observe runtime behavior — process creation, file system and registry changes, network communications, and API calls', 'generate behavioral reports for classification and IOC extraction' — with comprehensive coverage and no gaps.	5 / 5
Completeness	Explicitly answers both 'what' (detonate, observe, generate reports) and 'when' via a concrete 'Use when a sample has passed static triage..., when mapping a full infection chain, or when building YARA/behavioral signatures' trigger clause.	5 / 5
Trigger Term Quality	Strong natural keyword coverage ('malware samples', 'static triage', 'dynamic/behavioral analysis', 'infection chain', 'YARA/behavioral signatures', 'sandbox activity'), but lacks file-extension/synonym variants (e.g. .exe, .dll, PE, 'detonation'), keeping it just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (Cuckoo Sandbox, malware behavioral analysis, IOC/YARA extraction) with distinct triggers and minimal overlap risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
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/analyzing-malware-behavior-with-cuckoo-sandbox/SKILL.md
Commit: 9429fe9

Reviewed: 2 days ago

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