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analyzing-tls-certificate-transparency-logs

Queries Certificate Transparency logs via crt.sh and pycrtsh to detect phishing domains, unauthorized certificate issuance, and shadow IT. Monitors newly issued certificates for typosquatting and brand impersonation using Levenshtein distance. Use for proactive phishing domain detection and certificate monitoring.

68

Quality

61%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/analyzing-tls-certificate-transparency-logs/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Discovery

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 (CT log querying, phishing domain detection, typosquatting monitoring) with concrete tools (crt.sh, pycrtsh) and techniques (Levenshtein distance). It includes a 'Use for...' clause and occupies a very distinct niche in certificate transparency and phishing detection. The description is concise, uses third person voice, and provides excellent trigger terms for skill selection.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: querying Certificate Transparency logs via crt.sh and pycrtsh, detecting phishing domains, detecting unauthorized certificate issuance, detecting shadow IT, monitoring for typosquatting and brand impersonation using Levenshtein distance.

3 / 3

Completeness

Clearly answers 'what' (queries CT logs, detects phishing domains, monitors certificates for typosquatting) and 'when' ('Use for proactive phishing domain detection and certificate monitoring'). The 'Use for...' clause serves as an explicit trigger guidance.

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'Certificate Transparency', 'crt.sh', 'pycrtsh', 'phishing domains', 'typosquatting', 'brand impersonation', 'certificate monitoring', 'shadow IT', 'Levenshtein distance'. These cover the domain well and match what security professionals would naturally search for.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche: Certificate Transparency log querying, crt.sh, pycrtsh, and typosquatting detection via Levenshtein distance are very specific triggers unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Implementation

22%

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

The skill provides a basic starting point for querying crt.sh but fails to deliver on its core promise of typosquatting detection and certificate monitoring. The boilerplate sections waste tokens, while the genuinely novel and useful parts (Levenshtein distance calculation, CA validation, phishing infrastructure cross-referencing) are left as vague bullet points without implementation. The skill reads more like a high-level overview than actionable guidance.

Suggestions

Remove the generic 'When to Use' and 'Prerequisites' sections and replace with a lean header; use saved tokens to implement the Levenshtein distance typosquatting detection with executable code.

Add concrete code for flagging unexpected CAs (e.g., a CA whitelist comparison function) and for calculating similarity scores between discovered domains and the target brand.

Include a validation/triage workflow: after querying crt.sh, show how to filter results by date, score them by suspiciousness, and output a structured report (e.g., JSON with domain, similarity score, CA, issue date).

Add error handling for crt.sh API failures (rate limiting, timeouts) and a feedback loop for refining search patterns based on initial results.

DimensionReasoningScore

Conciseness

The 'When to Use' and 'Prerequisites' sections are padded with generic boilerplate that Claude already knows (e.g., 'Familiarity with security operations concepts', 'Access to a test or lab environment'). The key analysis steps are vague bullet points that restate the description rather than adding new information.

1 / 3

Actionability

Provides executable Python code for querying crt.sh, but the critical analysis steps (Levenshtein distance calculation, flagging unexpected CAs, cross-referencing phishing infrastructure) are described abstractly without concrete code or commands. The typosquatting detection—a core feature mentioned in the description—has no implementation.

2 / 3

Workflow Clarity

The 5 'key analysis steps' are listed but lack sequencing detail, validation checkpoints, or error handling. There's no guidance on what to do when suspicious certificates are found, no thresholds for Levenshtein distance, and no feedback loop for iterating on results. For a security monitoring workflow involving potential false positives, this is insufficient.

1 / 3

Progressive Disclosure

The content is organized into sections (When to Use, Prerequisites, Instructions, Examples), which provides some structure. However, there are no references to external files for advanced topics like Levenshtein implementation, CA whitelisting, or integration with SIEM/alerting systems, and the inline content is both too thin on detail and padded with boilerplate.

2 / 3

Total

6

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
mukul975/Anthropic-Cybersecurity-Skills
Reviewed

Table of Contents

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