analyzing-threat-landscape-with-misp

Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

80%

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

A concise, actionable skill body with a real executable script, weakened by a missing validation checkpoint in the batch workflow and an orphaned reference file that the body never links to.

Suggestions

Add a verification checkpoint to the workflow, e.g. after connecting confirm the event count is non-zero and after running validate that landscape_report.json contains expected keys before reporting success.

Link references/api-reference.md from the body (e.g. an 'API reference' line under Instructions) so the existing reference is discoverable instead of orphaned.

Note how to handle common failure modes (empty result set, auth/API-key errors, SSL issues via --no-ssl) to give the batch operation a feedback loop.

Dimension	Reasoning	Score
Conciseness	The body is lean and assumes Claude's competence — it never explains what MISP or IOCs are, and the Instructions are tight with a single copy-paste command. Not a 2 because there is no padded explanation to trim.	3 / 3
Actionability	Provides an executable install step and a complete copy-paste command ('python scripts/agent.py --misp-url ... --api-key ... --days 90 --output landscape_report.json') backed by a real, complete scripts/agent.py. Not a 2 because the guidance is fully executable rather than pseudocode.	3 / 3
Workflow Clarity	Steps are sequenced (install, configure, run) but this is a batch operation over many events with no validation/verification checkpoint (e.g. verify the API connection, sanity-check event count, validate the report), so workflow clarity is capped at 2. Not a 1 because the sequence is present and unambiguous.	2 / 3
Progressive Disclosure	Sections are well organized and scripts/agent.py is clearly signaled via the run command, but references/api-reference.md exists yet is never linked or referenced from the body, so a reference is present but not clearly signaled. Not a 3 because navigation to the existing reference file is missing; not a 1 because the overview itself is well structured and one path is signaled.	2 / 3
	Total	10 / 12 Passed

Description

82%

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 specific, well-scoped description with concrete actions and natural trigger terms, but it omits any explicit 'Use when...' trigger guidance, which caps completeness.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when analyzing a MISP instance's threat landscape, summarizing IOC distributions, or reporting on threat actor and malware-family trends over time.'

Surface a couple of common user phrasings (e.g. 'IOC analysis', 'threat intel summary', 'MISP report') to broaden natural-trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions such as 'querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends' and 'compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports', matching the multiple-specific-actions anchor.	3 / 3
Completeness	It clearly answers 'what' but contains no 'Use when...' clause or equivalent explicit trigger guidance, so per the judging guideline completeness is capped at 2. Not a 1 because the 'what' is thorough, and not a 3 because 'when' is entirely absent rather than explicit.	2 / 3
Trigger Term Quality	Uses natural CTI terms a user would actually say — 'threat landscape', 'MISP', 'IOC', 'threat actors', 'malware families' — giving good coverage rather than just jargon. Not scored lower because these are domain-natural, not overly generic.	3 / 3
Distinctiveness Conflict Risk	The MISP-specific framing ('Analyze the threat landscape using MISP', 'Uses PyMISP') carves a clear niche with distinct triggers unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 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
Commit: 673da1f

Reviewed: 13 days ago

Table of Contents

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