building-adversary-infrastructure-tracking-system

Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains matching known patterns. Use when pivoting from known indicators to discover related C2 infrastructure or maintaining a continuously updated map of a threat actor's network.

Quality

82%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

65%Weight 40%Scale 1-3

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 executable code and a clear step sequence, but it is verbose in its conceptual sections and fails to use the provided bundle files for progressive disclosure. Batch operations also lack validation feedback loops.

Suggestions

Trim the Overview/Key Concepts explanations and replace the generic 'When to Use' boilerplate with skill-specific triggers to improve conciseness.

Move the full implementation into scripts/agent.py and reference it from the body, and link references/api-reference.md from the relevant workflow steps so bundle files are actually used for progressive disclosure.

Add inline validation checkpoints and retry/error-recovery feedback loops for the batch API calls (e.g., handle non-200 responses and rate limits explicitly) to raise workflow clarity.

Dimension	Reasoning	Score
Conciseness	The Overview and Key Concepts re-explain domain material Claude largely already knows and the 'When to Use' list is generic boilerplate ('When establishing security controls aligned to compliance requirements'); the body is mostly efficient but could be tightened, so it sits below the lean anchor.	2 / 3
Actionability	The body provides complete, executable Python with real SecurityTrails endpoints, networkx graph operations, and report generation rather than pseudocode, matching the 'fully executable, copy-paste ready' anchor.	3 / 3
Workflow Clarity	Steps 1–3 are clearly sequenced but the batch API operations lack inline validation checkpoints or error-recovery feedback loops (non-200 responses silently return []), so per the batch-operation guideline workflow clarity is capped at 2.	2 / 3
Progressive Disclosure	The full implementation is inlined as a ~250-line wall of code and the existing bundle files (references/api-reference.md, scripts/agent.py) are never referenced or signaled from the body, so structure exists but content that should be separate is inline and references are not clearly signaled.	2 / 3
	Total	9 / 12 Passed

Description

100%Weight 40%Scale 1-3

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, names concrete capabilities and libraries, and includes an explicit 'Use when' trigger covering both what and when. It is clearly distinguishable and uses natural analyst terminology.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains') plus named libraries, matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Clearly states what it does (build/pivot/map/flag) and provides an explicit 'Use when...' trigger clause answering when to invoke it, satisfying both halves of the top anchor.	3 / 3
Trigger Term Quality	The 'Use when pivoting from known indicators to discover related C2 infrastructure or maintaining a continuously updated map of a threat actor's network' clause uses natural analyst phrasing (passive DNS, WHOIS, C2, pivoting) that a user would naturally say.	3 / 3
Distinctiveness Conflict Risk	The adversary C2 infrastructure pivoting niche is narrow with distinct triggers unlikely to fire for unrelated skills; not a level 2 because it does not overlap with generic analysis skills.	3 / 3
	Total	12 / 12 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/building-adversary-infrastructure-tracking-system/SKILL.md
Commit: e612f49

Reviewed: 7 days ago

Table of Contents

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