building-automated-malware-submission-pipeline

Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

65%

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

The skill is highly actionable with complete, executable code and a clear six-step pipeline, but it suffers from verbosity (entire implementations inlined) and missing validation/feedback checkpoints for risky batch operations. Progressive disclosure is underused: existing bundle files are neither linked nor leveraged to offload detail.

Suggestions

Replace the inlined full class implementations with concise snippets and point to scripts/agent.py and references/api-reference.md for the complete code, signalling those references explicitly in the relevant steps.

Add validation/verification checkpoints to the risky batch steps — e.g., confirm Splunk HEC ingestion, verify firewall block success, and handle/report errors in push_iocs_to_blocklist — to introduce a validate-then-continue feedback loop.

Tighten the Key Concepts, Tools & Systems, and Output Format sections to the essentials, assuming Claude's existing knowledge of sandboxing and IOC terminology.

Dimension	Reasoning	Score
Conciseness	The body inlines ~450 lines of full Python class implementations across all six steps plus tables, scenarios, and a mock report; it is mostly efficient and accurate but could be tightened by moving verbatim implementations to the bundled scripts/reference rather than reproducing them inline.	2 / 3
Actionability	Provides fully executable, copy-paste-ready Python with real API endpoints (VirusTotal, MalwareBazaar, Cuckoo, Splunk HEC) and complete classes, matching the executable-code anchor.	3 / 3
Workflow Clarity	The six-step sequence is clearly laid out, but this batch/destructive pipeline (automated malware handling, SIEM pushes, firewall blocking) lacks explicit validation checkpoints or error-recovery feedback loops, which caps workflow clarity per the batch-operations guideline.	2 / 3
Progressive Disclosure	Bundle files exist (references/api-reference.md and scripts/agent.py), but the body never signals or links to them — all content is inlined and the reference file largely duplicates the inlined code, so structure is present but references are not clearly surfaced.	2 / 3
	Total	9 / 12 Passed

Description

85%

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 well-constructed description that states concrete capabilities in third person and pairs them with an explicit 'Use when' trigger, satisfying both the what and when requirements. The only weakness is a somewhat narrow, scenario-specific trigger clause rather than broad natural keyword coverage.

Suggestions

Broaden the 'Use when' clause with more natural trigger phrasings users would actually say (e.g., 'malware analysis', 'sandbox submission', 'suspicious file triage', 'IOC extraction') instead of a single SOC-scaling scenario.

Consider mentioning common sandbox tool names (Cuckoo, Any.Run, VirusTotal) in the description itself to improve trigger matching against user requests that name those tools.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration' — matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers what the skill does (builds a pipeline that collects, submits, and generates verdicts) and when to use it via a clear 'Use when SOC teams need to scale malware analysis...' clause.	3 / 3
Trigger Term Quality	Includes relevant terms ('malware analysis', 'sandbox submissions', 'alert triage') but the trigger is framed around a narrow SOC scaling scenario rather than broad natural phrasings a user would say, missing common variations.	2 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (automated malware submission/analysis pipeline) with distinct triggers and third-person voice ('Builds'), making it unlikely to conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (508 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: mukul975/Anthropic-Cybersecurity-Skills
Commit: 673da1f

Reviewed: 13 days ago

Table of Contents

Discovery Implementation Validation

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