Incident Validation Correlation logo

Incident Validation Correlation

Organization
zero-lab-ai
incident-validation-correlation

Correlates supplied sanitized incident observations with separately approved isolated defensive-validation receipts across prevent, observe, detect, contain, and recover. Use for defensive learning comparison without live response, hunting, technique design or execution, detection authoring, case management, findings, or reporting.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill nameincident-validation-correlation
Stars
232
Forks
14
Bundled files
2
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.

  • 2 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 Incident Validation Correlation 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/incident-validation-correlation
  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/incident-validation-correlation .claude/skills/incident-validation-correlation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Validation Correlation 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 Incident Validation Correlation 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 Incident Validation Correlation 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.

Incident validation correlation

When to Use

  • Correlate incident evidence with validation results.
  • 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.

Purpose

Answer one question: is an exact, pre-approved isolated validation genuinely comparable to a supplied sanitized incident observation, and what defensive learning survives? A complete ledger does not close an incident, verify remediation, establish attribution or scope, or prove coverage.

Boundary

Never contact assets, identities, sources, responders, or attackers. Never hunt, respond, validate production, design or execute techniques, author detections, manage cases, create findings, or write reports. Reject hostnames, addresses, accounts, tenants, domains, paths, victim identities, IOCs, raw events, packets, memory, malware, credentials, tickets, certificates, commands, scripts, queries, rules, payloads, technique procedures, tool syntax, infrastructure, and operational timelines. Use sanitized opaque references and digests only.

Workflow

  1. Accept only supplied incident packages and supplied, immutable, pre-approved validation cases and receipts.
  2. Stop on restricted content. Never quote a rejected value.
  3. Preserve separate incident acceptance and validation execution authority.
  4. Report only generic diagnostic codes; never reflect restricted values.
  5. Require exactly one observation and one pre-approved case for each supplied learning item and objective.
  6. Require observation time before learning creation and independent validation approval after learning creation.
  7. Require exact semantic-equivalence binding across observation class, immutable fixture, objective, environment limitations, and expected benign effect. Taxonomy similarity is not equivalence.
  8. Mark weak equivalence not-comparable. Never claim that a lab reproduces a production incident or that production predicts lab behavior.
  9. Keep prevent, observe, detect, contain, and recover comparisons separate.
  10. Keep observation, execution, benign-effect, telemetry, alert, containment, recovery, cleanup, custody, and policy evidence roles separate. Require the closed role/outcome matrix; labels never override contradictory evidence. Consume every supplied authority and environment exactly once.
  11. An alert is not complete telemetry or control evidence. A validation alert is not incident reproduction.
  12. Treat alert absence under unhealthy telemetry as inconclusive.
  13. Accept only isolated-lab or dedicated-test-tenant validation receipts.
  14. Treat a fired stop as terminal. Allow only later cleanup, custody, and withdrawal; retry needs a new separately approved case. Authority expiry, revocation, fixture drift, and environment drift are also terminal: no later execution, evidence, or result is valid.
  15. Require exact current policy proof and a closed NA reason code for not-applicable. Keep every objective in the denominator.
  16. Keep handoff occurrence, approval, authority, result, and withdrawal separate with typed exact receipts. Expiry, revocation, fixture drift, environment drift, and a fired stop require withdrawal. A handoff never grants authority. Custody precedes every consuming result, gap, or handoff receipt; withdrawal evidence is exact for its trigger.
  17. Interpret complete as correlation complete only. Never call the incident closed or remediation verified.
  18. Return comparison states, cycle states, learning-gap IDs, and handoff states. Never assign severity, compliance, or detection coverage.

Report

State the ledger identifier, exact objective denominator, comparison and cycle counts, learning gaps, and handoff states. Add: “Correlation consistency only; no live interaction, incident closure, remediation verification, attribution, severity, or coverage claim occurred.”

Rationalizations to reject

  • “The taxonomy matches.” Similar labels do not establish equivalence.
  • “The lab reproduced production.” Environment limits survive correlation.
  • “The alert proves detection.” Alert evidence is only one role.
  • “No alert means a gap.” Unhealthy telemetry makes the result inconclusive.
  • “The stop cleared, so retry.” A stop is terminal for that approved case.
  • “Cleanup means validation succeeded.” Cleanup proves only cleanup.
  • “The handoff approved execution.” Handoffs transfer defensive learning only.
  • “Complete means the incident is closed.” It means correlation complete.

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 Incident Validation Correlation AI skill do?

Correlates supplied sanitized incident observations with separately approved isolated defensive-validation receipts across prevent, observe, detect, contain, and recover. Use for defensive learning comparison without live response, hunting, technique design or execution, detection authoring, case management, findings, or reporting.

Why use Incident Validation Correlation on TypingMind?

Because you install it once and use it with any model. Incident Validation Correlation 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 Incident Validation Correlation 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/incident-validation-correlation. 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 Incident Validation Correlation?

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 Incident Validation Correlation?

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

Is the Incident Validation Correlation 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇