analyzing-email-headers-for-phishing-investigation

Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing. Use when triaging a suspicious or reported email, investigating a phishing incident, or verifying whether a message's sender domain was spoofed.

Quality

82%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

High

Do not use without reviewing

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 content is highly actionable with executable code and a clear step sequence, but it is somewhat verbose and monolithic. Bundle files are not surfaced from the body, and validation checkpoints are absent in the forensic workflow.

Suggestions

Add explicit validation checkpoints to the workflow (e.g., verify the Received chain is complete and SPF/DKIM/DMARC results are consistent before concluding spoofing), with fix-and-retry guidance.

Move or de-duplicate the Key Concepts table and large reference snippets into references/api-reference.md, and link to it from the body so the SKILL.md stays a lean overview.

Reference scripts/agent.py from the workflow (e.g., 'For automated end-to-end analysis, run scripts/agent.py <eml>') so the bundled tool is discoverable.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with task-relevant executable code, but the Key Concepts table explains SPF/DKIM/DMARC concepts Claude already knows and several code comments restate the obvious; could be tightened.	2 / 3
Actionability	Provides fully executable Python (email, pyspf, Levenshtein, hashlib) and shell (dig, whois, curl) snippets with specific real-looking IPs, domains, and selectors — copy-paste ready.	3 / 3
Workflow Clarity	A clear 5-step sequence is present, but a forensic/evidence-handling workflow involving reputation lookups and batch extraction lacks explicit validation checkpoints or feedback loops, which caps the score.	2 / 3
Progressive Disclosure	The body is a single ~300-line monolithic doc with all code inline; bundle files (references/api-reference.md, scripts/agent.py) exist but are never referenced or linked from the body, so navigation is not signaled.	2 / 3
	Total	9 / 12 Passed

Description

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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 natural trigger terms, and clearly states both what the skill does and when to use it with an explicit 'Use when' clause. It is third-person, concise, and occupies a distinctive niche with low conflict risk.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Parse and analyze email headers (Received chain, Return-Path, Message-ID)', 'validate SPF, DKIM, and DMARC results', 'confirm or rule out sender spoofing' — rather than vague language.	3 / 3
Completeness	Explicitly answers what ('trace the true origin... validate SPF, DKIM, and DMARC') and when via an explicit 'Use when triaging a suspicious or reported email...' trigger clause.	3 / 3
Trigger Term Quality	Uses natural phrases a triage analyst would say — 'phishing email', 'phishing incident', 'suspicious or reported email', 'sender domain was spoofed' — giving good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	Has a clear niche (phishing email header analysis plus SPF/DKIM/DMARC authentication) with distinct triggers unlikely to overlap with other skills.	3 / 3
	Total	12 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 1 missing	Warning

	Total	14 / 16 Passed

Repository: mukul975/Anthropic-Cybersecurity-Skills
Path: skills/analyzing-email-headers-for-phishing-investigation/SKILL.md
Commit: e612f49

Reviewed: 6 days ago

Table of Contents

Discovery Implementation Validation

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