analyzing-campaign-attribution-evidence
analyzing-campaign-attribution-evidence is a Blue team & detection skill for AI agents, published by mukul975 in the Anthropic-Cybersecurity-Skills repository on GitHub. Skillspace rates analyzing-campaign-attribution-evidence B (81/100) and it passed the safety scan; a judge model has also reviewed it.
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 32,716
Placed #2 of 6 in the judged category final for Blue team & detection.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #2 of 6 in the judged category final for Blue team & detection.
- Trigger
88 88 - Instructions
85 85 - Specificity
88 88 - Safety
92 92 - Disclosure
55 55 - Maintenance
45 45
A working AttributionAnalyzer class implements Diamond Model/ACH scoring with concrete confidence thresholds (80/40) and correct tradecraft like PDB-path code-similarity artifacts, though it is one long undivided file that still opens with the same generic four-bullet boilerplate as its thinner siblings.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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