Soc Metrics

Organization

gensecaihq

soc-metrics

Calculate and interpret SOC performance metrics — MTTD, MTTA, MTTR, MTTC, alert-to-incident ratio, false-positive and automation rates, and agent cost/latency — with maturity context; use for KPI reporting and SOC performance questions.

Overview

Publisher	gensecaihq
Repository	`Wazuh-Autopilot`
Skill name	`soc-metrics`
Stars	61
Forks	15
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 Soc Metrics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/soc-metrics
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.

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/soc-metrics .claude/skills/soc-metrics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Soc Metrics 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 Soc Metrics 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 Soc Metrics 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.

SOC Metrics

Metrics should drive improvement, not vanity. Define precisely, compute consistently, and always show the trend and the sample size.

Time metrics (per incident, then median and p90 over the period)

Metric	Definition	Start → End
MTTD — mean time to detect	how long the attacker was active before detection	first malicious activity → first alert
MTTA — mean time to acknowledge	time until someone (agent or human) started work	case created → first triage finding
MTTC — mean time to contain	time to stop the harm	case created → containment verified
MTTR — mean time to respond/resolve	time to close out	case created → resolved/closed

Report median and p90 alongside the mean — a few long incidents skew means. MTTD requires an incident timeline; mark it "n/a" where the start of malicious activity is unknown.

Volume and quality

Metric	Formula	Healthy signal
Alert-to-incident ratio	alerts ingested ÷ cases opened	falling over time with stable coverage
False-positive rate	cases closed `false_positive` ÷ cases closed	trending down; > 50% means tuning needed
Escalation rate	cases reaching investigation ÷ cases opened	stable, explainable
Automation rate	actions auto-approved or auto-executed ÷ all executed actions	increases only with low rollback rate
Rollback rate	actions rolled back ÷ executed	< 5%; spikes mean over-aggressive autonomy
Verification rate	executed actions verified ÷ executed	→ 100%
Approval latency	action proposed → approved (median)	within policy expiry
Agent coverage	active Wazuh agents ÷ expected assets	→ 100%
Telemetry loss	alerts for rule 203 (event queue full) or rule 204 (queue flooded), and agents disconnected for > 1h (rule 504)	zero; any non-zero means detection blind spots. Get detail from `platform-engineer`
MTTD	first attacker event time (from the log, not the alert `timestamp`) → case opened	falling; ingestion delay shows up as the gap between event time and alert time

Agent (swarm) performance

Per agent: runs, error rate, median/p95 latency, tokens in/out, cost per run and per incident. Flag agents whose cost per incident rises without better outcomes, or whose error rate > 5%.

Interpretation

Always compare with the previous equal period and state the sample size.
A metric moving the "good" way can be bad: a falling FP rate plus falling incidents may mean a detection broke. Cross-check with alert volume and agent coverage.
SOC-CMM style maturity hints: consistent metric definitions, trend review in a regular meeting, and metrics tied to improvement actions indicate a managed (level 3+) process; ad-hoc or unreviewed metrics indicate initial/defined levels.

Output format


| Metric | This period | Previous | Change | n |

Follow with 3 bullet insights and 1–3 recommendations.

Output

save_report(kind="weekly"|"monthly", title, body_md) or include the table in the report being written by executive-reporting. standard_refs: NIST-CSF-2:ID.IM (improvement).

Frequently asked questions

What does the Soc Metrics AI skill do?

Why use Soc Metrics on TypingMind?

Because you install it once and use it with any model. Soc Metrics 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 Soc Metrics in TypingMind?

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

Which AI models can use Soc Metrics?

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 Soc Metrics?

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

Is the Soc Metrics 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

SOC Metrics

Time metrics (per incident, then median and p90 over the period)

Volume and quality

Agent (swarm) performance

Interpretation

Output format

Output

Frequently asked questions

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