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Campaign Attribution Evidence Analysis
Professional CTI analysis skill for structured attribution using ACH, Diamond Model, and confidence scoring.
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See it in action
You say
We detected a intrusion last month. The TTPs match APT29 based on our analyst notes and there is infrastructure overlap with a known campaign. I also have a MISP event export. Assess the attribution evidence and produce an analyst report with an ACH matrix and confidence statement for our CTI team.
Your agent does
The skill confirms scope and audience, builds an evidence register from the analyst notes and MISP export classifying each item by category, source, date, confidence, and independence, then applies the Diamond Model to structure adversary, capability, infrastructure, and victim evidence. The ACH matrix tests each evidence item against APT29, an alternative actor hypothesis, and an unknown actor hypothesis, with consistency and inconsistency ratings for each. The attribution confidence assessment explains why the confidence level is moderate rather than high given the infrastructure overlap may indicate shared tooling rather than unique actor attribution. The report includes key supporting evidence, key inconsistent evidence, alternative explanations, intelligence gaps, and recommended next steps for the CTI team.
What you get
About this skill
High-Confidence Threat Actor Attribution
Moving from a "hunch" to a defensible attribution assessment is one of the hardest tasks for CTI analysts. This skill provides a rigorous, analytical framework to evaluate cyber campaign evidence, helping you move beyond simple indicator matching to structured intelligence analysis.
What it does
This skill processes CTI reports, incident artifacts, and TTPs to generate a comprehensive attribution profile. It uses industry-standard methodologies to ensure your findings are objective and resilient to scrutiny. It supports:
- Diamond Model Mapping: Structuring adversary, capability, infrastructure, and victim relationships.
- Analysis of Competing Hypotheses (ACH): Testing evidence against multiple actor profiles to eliminate bias.
- Confidence Scoring: Assigning standardized ratings (High/Med/Low) based on source provenance and evidence strength.
- Evidence Register: Building a traceable log of indicators, malware overlaps, and timing patterns.
Why use this skill?
Prompting an AI for attribution often results in "hallucinated" certainty or generic summaries. This skill enforces analytical rigor by requiring alternative hypotheses and identifying intelligence gaps. It filters out weak signals like publicly available tools or easily faked language artifacts, ensuring your report holds up in a briefing or board meeting.
Output
The output is a structured analyst report including an ACH matrix, a Diamond Model summary, a formal confidence statement, and defensive recommendations for SOC and IR teams.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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