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Severity Scoring

Organization
gensecaihq
severity-scoring

Compute a defensible priority (P1–P4), severity and confidence for a case from rule level, asset criticality, blast radius and ATT&CK tactic; use whenever you set or change case severity.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill nameseverity-scoring
Stars
61
Forks
15
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by gensecaihq on GitHub. Read the source before you install it.

Installation

Install the Severity Scoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/severity-scoring
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gensecaihq/Wazuh-Autopilot.git /tmp/Wazuh-Autopilot
mkdir -p .claude/skills
cp -r /tmp/Wazuh-Autopilot/backend/app/skills/severity-scoring .claude/skills/severity-scoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Severity Scoring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Severity Scoring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Severity Scoring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Severity Scoring

Severity must be explainable. Use this formula, then sanity-check it.

Inputs (each scored 0–10)

FactorWeightHow to score
Rule level (L)0.30min(10, rule.level * 10 / 15) using the highest level in the case (see note below)
Asset criticality (A)0.2510 crown-jewel / domain controller / prod DB; 7 prod server; 4 internal workstation; 2 lab/test
Tactic weight (T)0.20see table below, highest tactic present
Blast radius (B)0.151 host = 2; 2–5 hosts = 5; >5 hosts or domain-wide identity = 9; org-wide = 10
Evidence strength (E)0.1010 confirmed success (e.g. login after brute force, file dropped); 5 attempt; 2 anomaly only

score = 0.30L + 0.25A + 0.20T + 0.15B + 0.10E

About Wazuh levels

Wazuh levels describe the rule, not your environment. Some are misleading in context:

  • High-level rules can be environmental or operational. For example, rule 204 (agent event queue flooded, level 12) is a pipeline problem, and rule 1003 (oversized syslog message, level 13) is often a misbehaving device. Keep L from the level, but score E low unless there is attacker evidence.
  • Low-level rules can be the key evidence. For example, rule 5715 (sshd authentication success, level 3) right after rule 5712 (brute force, level 10) from the same source is a likely compromise. Score E = 10 and use the highest level in the case, not the success event's.
  • Vulnerability-detector levels follow CVE severity (rules 23503, 23504, 23505, 23506 = Low, Medium, High, Critical). That is exposure, not activity: route to vulnerability-prioritization instead of scoring it as an intrusion.

Tactic weights (MITRE ATT&CK)

TacticWeight
Reconnaissance, Resource Development2
Initial Access (attempt), Discovery4
Execution, Persistence, Defense Evasion6
Credential Access, Privilege Escalation7
Lateral Movement, Command and Control8
Collection, Exfiltration9
Impact10

Mapping

ScorePrioritySeverityTarget response
≥ 8.0P1criticalimmediate, page on-call
6.0–7.9P2highwithin 1 hour
4.0–5.9P3mediumsame business day
< 4.0P4low / informationalbacklog / batch

Overrides (apply after the formula):

  • Confirmed data exfiltration or ransomware behaviour → P1 regardless.
  • Only reconnaissance with no success, external source → cap at P3.
  • Asset tagged protected/critical in org context → floor at P2.

Confidence (0–1)

Confidence is how sure you are the activity is malicious, separate from severity.

EvidenceConfidence
Multiple independent signals + threat-intel match0.9–1.0
Clear pattern (e.g. brute force then success)0.75–0.9
Single high-fidelity rule0.6–0.75
Single generic rule / anomaly0.3–0.6
Likely benign, unexplained< 0.3

perform_risk_assessment may be used as an extra input; don't let it override your own reasoning without explanation.

Output

  • update_case(case_id, severity=..., confidence=...).
  • add_finding titled Severity rationale containing the factor table with scores, the computed score, overrides applied, priority, and confidence reasoning. standard_refs: NIST-800-61r3.

Frequently asked questions

What does the Severity Scoring AI skill do?

Compute a defensible priority (P1–P4), severity and confidence for a case from rule level, asset criticality, blast radius and ATT&CK tactic; use whenever you set or change case severity.

Why use Severity Scoring on TypingMind?

Because you install it once and use it with any model. Severity Scoring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Severity Scoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/severity-scoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Severity Scoring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Severity Scoring?

As many as you like. As long as a model supports skills, you can use Severity Scoring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Severity Scoring AI skill free?

Yes. It is published on GitHub by gensecaihq under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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