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

71%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/building-automated-malware-submission-pipeline/SKILL.md

Quality

Content

42%

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

The skill provides highly actionable, executable Python code covering a complete malware analysis pipeline from collection through SIEM integration. However, it is excessively verbose — explaining concepts Claude already knows, including unnecessary tool descriptions, and inlining all code in a monolithic document. The workflow lacks validation checkpoints and error handling critical for a pipeline that pushes IOCs to blocking infrastructure.

Suggestions

Remove the 'Key Concepts' glossary table and 'Tools & Systems' descriptions entirely — Claude already knows these terms and tools.

Add explicit validation checkpoints: verify API responses before proceeding, validate sandbox report completeness before IOC extraction, and add a confirmation/dry-run step before pushing IOCs to blocklists.

Split the large code blocks into referenced bundle files (e.g., collector.py, screener.py, submitter.py) and keep SKILL.md as a concise overview with the orchestration logic and workflow steps.

Remove the 'Common Scenarios' section which largely duplicates the 'When to Use' section.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~350+ lines. The 'Key Concepts' table explains terms like 'Dynamic Analysis' and 'Static Analysis' that Claude already knows. The 'Tools & Systems' section describes well-known tools unnecessarily. The 'Common Scenarios' section restates the 'When to Use' section. Much of the code could be tightened significantly.	1 / 3
Actionability	The code is concrete, executable Python with real API endpoints, proper class structures, and specific library usage. Each step provides copy-paste ready code with actual API calls to CrowdStrike, VirusTotal, MalwareBazaar, Cuckoo, and Splunk HEC.	3 / 3
Workflow Clarity	The 6-step workflow is clearly sequenced and the orchestration function ties steps together logically. However, there are no explicit validation checkpoints — no error handling for failed API calls, no verification that sandbox submissions succeeded before proceeding, and no feedback loops for retrying failed analyses. For a pipeline involving destructive actions like pushing IOCs to blocklists, this is a significant gap.	2 / 3
Progressive Disclosure	All content is monolithically inlined in a single file with no references to external files. The extensive code for 6 different classes/functions, the glossary table, tools descriptions, and output format example all live in one massive document. This would benefit greatly from splitting code into referenced files and keeping SKILL.md as an overview.	1 / 3
	Total	7 / 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.

This is a strong skill description that clearly articulates specific capabilities (file collection, sandbox submission, verdict generation with IOCs), includes relevant domain terminology that SOC analysts would naturally use, and provides an explicit 'Use when' clause with clear trigger conditions. The description is well-structured, uses third person voice appropriately, and occupies a distinct niche that minimizes conflict risk with other skills.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: collects suspicious files from endpoints and email gateways, submits to sandbox environments and multi-engine scanners, generates verdicts with IOCs for SIEM integration. These are detailed, concrete capabilities.	3 / 3
Completeness	Clearly answers both 'what' (builds automated malware submission and analysis pipeline with specific capabilities) and 'when' (explicit 'Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage').	3 / 3
Trigger Term Quality	Includes strong natural keywords that SOC analysts would use: 'malware', 'sandbox', 'IOCs', 'SIEM', 'alert triage', 'malware analysis', 'endpoints', 'email gateways', 'multi-engine scanners'. Good coverage of domain-specific terms users would naturally mention.	3 / 3
Distinctiveness Conflict Risk	Highly specific niche combining malware analysis pipeline, sandbox submission, IOC generation, and SIEM integration. This is unlikely to conflict with other skills due to its very targeted security operations domain focus.	3 / 3
	Total	12 / 12 Passed

Validation

81%

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 — 9 / 11 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	9 / 11 Passed

Repository: mukul975/Anthropic-Cybersecurity-Skills
Commit: 0445030

Reviewed: 12 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.