Detection Validate logo

Detection Validate

Organization
zero-lab-ai
detection-validate

Runs attacker techniques and confirms whether detections actually fire, producing an honest ATT&CK coverage map that separates real gaps from accepted ones. Use when running a purple-team exercise, measuring detection coverage, validating that a SOC can see a technique, or turning 'we think we're covered' into evidence per technique.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namedetection-validate
Stars
232
Forks
14
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Detection Validate 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/zero-lab-ai/skills/tree/main/plugins/detection-validation/skills/detection-validate
  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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/detection-validation/skills/detection-validate .claude/skills/detection-validate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Detection Validate 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 Detection Validate 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 Detection Validate 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.

Detection validation

Purple team answers one question, per technique: we executed it — did a detection fire? Everything else is commentary.

"We think we're covered" is not coverage. Coverage is a specific claim about specific techniques, backed by a recorded outcome for each. A map with blanks in the detection column is the same lie as a port scan that came back clean with zero coverage — a confident summary over an unanswered question.

When to Use

  • Running a purple-team exercise, measuring detection coverage, validating that a SOC can see a technique, or turning 'we think we're covered' into evidence per technique.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Every executed technique gets exactly one honest outcome

OutcomeMeaning
coveredexecuted, a detection fired
partialfired weakly, or on only some variants
not-detectedexecuted, nothing fired — a real gap
not-detectablepassive technique with no observable (OSINT, reading public records) — an accepted gap, not a failure
unknownexecuted but the outcome was never recorded — the one that poisons the report

The distinction that matters: not-detectable is honest (you looked, there is nothing to see, you accept it) while unknown is a blank wearing a confident coverage number. They must never be conflated.

Score the map

sh
uv run {baseDir}/scripts/coverage_map.py map.json

Computes real coverage as covered+partial over everything executed that could be detected — passive not-detectable techniques are removed from the denominator. Exit 0 only when every executed technique has a known, non-gap outcome; 1 when any not-detected or unknown remains; 2 on bad or empty input. See references/coverage-map.md.

Validation record

For each technique, return the technique and variant, execution receipt, expected observable, telemetry-health evidence, alert evidence, one outcome from the table above, limitations, and an owner for any gap. Keep the execution receipt and alert receipt separate: the same timestamp or operator note cannot prove both sides of the join.

End with counts for covered, partial, not-detected, not-detectable, and unknown, plus the denominator used. Never publish only a percentage.

Pair with the attacker and the detector

This skill is the join between offense and defense:

  • The offensive technique comes from the operator (or an attack simulation). If you cannot execute it, you cannot validate its detection — an untested technique is unknown, never assume covered.
  • The detection that should fire is engineered and proven separately. A rule that passed its own true-positive test in isolation can still fail to fire in a live exercise against the real technique variant — that is exactly what this measures, and why the exercise is not redundant with the rule's unit test.

Rationalizations to reject

ThoughtReality
"We ran 40 techniques, we're covered."Not until all 40 have a recorded outcome. Blanks are unknown, and unknown is not coverage.
"No alert fired, so the technique must be benign."No alert fired means you did not detect it. That is a gap, not an all-clear.
"It's probably detected, the rule exists."A rule existing is not a rule firing. The exercise exists because rules fail live that passed in isolation.
"Mark the passive stuff as covered so the number looks complete."not-detectable is removed from the denominator, not counted as covered. Honest beats complete.
"We'll fill in the blanks later."A blank is unknown now. Ship the map honest with the gaps visible, or re-run the technique.
"100% coverage."Almost always means the un-run techniques were left off the map. Coverage is over what you executed, and the map must show what you did not.

References

  • Coverage map format — the JSON the scorer consumes, the five outcomes, and how the denominator is computed

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Detection Validate AI skill do?

Runs attacker techniques and confirms whether detections actually fire, producing an honest ATT&CK coverage map that separates real gaps from accepted ones. Use when running a purple-team exercise, measuring detection coverage, validating that a SOC can see a technique, or turning 'we think we're covered' into evidence per technique.

Why use Detection Validate on TypingMind?

Because you install it once and use it with any model. Detection Validate 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 Detection Validate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/detection-validation/skills/detection-validate. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Detection Validate?

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 Detection Validate?

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

Is the Detection Validate AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 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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