analyzing-campaign-attribution-evidence

Overview of analyzing-campaign-attribution-evidence skill

What this skill is for

The analyzing-campaign-attribution-evidence skill helps you turn scattered threat-intel clues into a defensible campaign attribution assessment. It is built for analysts who need to weigh evidence, not just list indicators: infrastructure overlap, ATT&CK consistency, malware similarity, timing patterns, and language artifacts.

Best-fit users and use cases

Use the analyzing-campaign-attribution-evidence skill when you are doing CTI work, incident analysis, or an analyst review for Security Audit where the question is “who is most likely behind this campaign, and how confident are we?” It is most useful when you already have partial evidence and need structured reasoning.

What makes it different

The skill is opinionated around Diamond Model and ACH-style analysis, so it is better for evidence weighting than for generic threat summaries. It also aligns with STIX, TAXII, and MITRE ATT&CK concepts, which makes it easier to plug into a real CTI workflow instead of an isolated prompt.

How to Use analyzing-campaign-attribution-evidence skill

Install and load it

For analyzing-campaign-attribution-evidence install, use the repo path directly:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-campaign-attribution-evidence

After install, read skills/analyzing-campaign-attribution-evidence/SKILL.md first, then check:

• references/workflows.md for the end-to-end analysis flow
• references/api-reference.md for Diamond Model and ACH scoring structure
• references/standards.md for STIX, ATT&CK, and TLP context
• assets/template.md for report output shape
• scripts/process.py and scripts/agent.py for practical logic and weighting

What input the skill needs

The analyzing-campaign-attribution-evidence usage pattern works best when you provide:

• a named incident or campaign
• candidate threat actors or a hypothesis set
• evidence categories you actually have
• your confidence level for each item
• the decision you need to make: attribution, prioritization, briefing, or Security Audit support

Stronger input looks like: “Compare APT29 vs. UNC2452 using infrastructure overlap, TTPs, timing, and malware reuse from the last 30 days. Produce a confidence-weighted assessment and note missing evidence.”

Practical workflow for better results

Start by normalizing evidence into categories, then ask the skill to map each item to a hypothesis. If you are uncertain, request a matrix-style comparison first and a narrative conclusion second. That reduces premature certainty and makes gaps visible.

Repository-reading path that saves time

If you only read a few files, read them in this order:

1. SKILL.md for intent and constraints
2. references/workflows.md for process
3. references/api-reference.md for scoring and evidence logic
4. scripts/process.py to understand how inputs are expected
5. assets/template.md to format the final report

analyzing-campaign-attribution-evidence skill FAQ

Is this only for Security Audit work?

No. The analyzing-campaign-attribution-evidence for Security Audit use case is a strong fit, but the skill also supports CTI reporting, threat hunting validation, and post-incident attribution analysis.

Is it better than a normal prompt?

Usually yes, if you need consistent reasoning. A generic prompt may summarize evidence, but this skill is designed to force structured comparison across hypotheses and reduce ad hoc attribution claims.

What should I avoid using it for?

Do not use it when you have almost no evidence or when the real task is basic IOC triage. If you only need a simple incident summary, the attribution workflow is overkill and may create false confidence.

Is it beginner-friendly?

Yes, if you can provide a clear incident summary and a small evidence set. Beginners may still need help naming hypotheses, but the skill is useful because it shows what evidence matters and what is still missing.

How to Improve analyzing-campaign-attribution-evidence skill

Give cleaner evidence, not more prose

The skill performs better when you separate facts from interpretation. Provide bullet lists for infrastructure, malware, ATT&CK techniques, timestamps, victimology, and language markers. Avoid mixing “we think” statements into raw evidence.

Name competing hypotheses explicitly

The biggest quality gain comes from telling the skill what it is comparing. Instead of “analyze attribution,” use two to four candidate actors or clusters. That lets analyzing-campaign-attribution-evidence compare consistency, inconsistency, and neutral evidence instead of guessing the frame.

Ask for confidence and gaps

To improve analyzing-campaign-attribution-evidence usage, request:

• a scored hypothesis table
• a short explanation for each strong signal
• missing evidence that would change the conclusion
• a final confidence statement with caveats

This is especially useful when the output will be reviewed by Security Audit, legal, or leadership.

Iterate from matrix to narrative

If the first answer is too broad, ask for a tighter ACH matrix or a Diamond Model pivot view before asking for the final report. Then refine by adding new evidence, removing weak signals, or narrowing the actor set.