Dt Sec Semantic Mapping

Organization

Dynatrace

dt-sec-semantic-mapping

Suggest and validate semantic dictionary (SD) mappings for new security integrations using vendor API samples or live events. Use when: mapping a new security vendor data to Dynatrace SD; checking required fields; validating namespaces; highlighting discrepancies vs the semantic dictionary; proposing mapping improvements; running runtime validation against live tenant data.

Overview

Publisher	Dynatrace
Repository	`dynatrace-for-ai`
Skill name	`dt-sec-semantic-mapping`
Stars	156
Forks	30
Bundled files	20
License	Apache-2.0
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.
20 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Dynatrace on GitHub. Read the source before you install it.

Installation

Install the Dt Sec Semantic Mapping 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.
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/Dynatrace/dynatrace-for-ai.git /tmp/dynatrace-for-ai
mkdir -p .claude/skills
cp -r /tmp/dynatrace-for-ai/skills/dt-sec-semantic-mapping .claude/skills/dt-sec-semantic-mapping

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

Use it in TypingMind

Enable Dt Sec Semantic Mapping 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 Dt Sec Semantic Mapping 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 Dt Sec Semantic Mapping 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.

dt-sec-semantic-mapping

Build and validate semantic-dictionary-aligned mappings for new security integrations.

Purpose

Use this skill when a user wants to:

Suggest a mapping from vendor API output to Dynatrace security.events fields (Workflow A).
Validate an existing mapping for completeness and quality against:
- Local baseline samples and semantic dictionary (Workflow B1 — static, offline validation), or
- Live tenant data via live tenant access (Workflow B2 — runtime validation)
Highlight discrepancies vs. the Semantic Dictionary and local references.
Get actionable mapping improvements.

Semantic Dictionary

The Semantic Dictionary (SD) defines the canonical field set for security.events. See references/semantic-reference.md for the canonical reference: local-vs-live sources, queryable Grail tables, when-to-query decision matrix, and the authority rule (live SD wins on disagreement).

Required Inputs

Always run the intake checklist in references/intake-and-constraints.md before generating or validating a mapping. If inputs are incomplete, continue with a partial draft but explicitly list missing evidence and confidence limits.

Baseline Sources (self-contained)

All baseline material lives inside this skill:

samples/ — real integration payloads covering all finding types and providers. Consulted as a fallback when primary references (SD, data-model-notes, known-discrepancies, validation-rules, object-type-expectations) leave a specific question unresolved — not as a routine step on every workflow run.
references/semantic-reference.md — SD reference, field taxonomy, event types, provider taxonomy, and entity scoping
references/validation-policy-and-reporting.md — validation rules, acceptable discrepancies, and report templates
references/intake-and-constraints.md — intake checklist, output contract, object.type expectations, and OpenPipeline constraints

Event-Type Coverage Requirements

The mapping MUST address the correct set of event.type values per finding class. Detection integrations are push-based and do not use scan cycles — never require scan events for detection.

See validation-policy-and-reporting.md § Event-Type Coverage for the full table, severity rules, and the alternative-classification path when a detection-class mapping incorrectly emits *_SCAN events.

Workflows

This skill operates in three modes. Detect the mode from context:

Mode	Input	Procedural source
Workflow A — Suggest a new mapping	Raw vendor API payloads only	`references/mapping-workflow.md § Workflow A` (Phase 1 mapping table → user approval → Phase 2 sample JSON)
Workflow B1 — Static validation	Existing mapping + vendor API samples	`references/mapping-workflow.md § Workflow B` — classify input mode (final ingested / theoretical), apply rules, produce diff-highlighted table
Workflow B2 — Runtime validation	Existing mapping + live tenant access	`references/runtime-validation.md` — load the security (AppSec) events supporting skill first (REQUIRED Step 0), then run the query pack, produce a Validation Summary table

All workflows follow the output contracts in references/intake-and-constraints.md and the report templates in references/validation-policy-and-reporting.md. Validation rules (event-type coverage, required fields, scan references, namespace requirements, value/type checks, vendor-namespace duplication) live in references/validation-policy-and-reporting.md.

Acceptable Discrepancy Policy

See references/validation-policy-and-reporting.md for the canonical list of acceptable SD deviations and vendor-namespace patterns. Do NOT raise critical/major issues for fields on that list. Genuinely unknown fields (not in local refs AND not in the live SD — see references/semantic-reference.md) must be questioned per references/validation-policy-and-reporting.md.

Scope

This skill covers:

Mapping suggestion and refinement (Workflow A).
Static validation against local baseline examples and semantic dictionary (Workflow B1).
Runtime validation via live tenant access against live tenant data (Workflow B2).
Semantic-dictionary conformance checks.
Gap analysis and improvement recommendations.

This skill does not cover:

Live ingestion pipeline deployment.
Runtime DQL performance benchmarking.
Tenant-side ingestion troubleshooting.

References

references/semantic-reference.md — SD reference plus data-model notes: local sources, live (queryable) sources, DQL patterns, when-to-query decision matrix, authority rule, field taxonomy
A skill covering full SD access patterns and Grail-table documentation — for DQL query patterns against security.events and Grail tables
Semantic Dictionary (public docs)
references/intake-and-constraints.md — intake checklist, output contract, OpenPipeline constraints, and object.type namespace expectations
references/mapping-workflow.md — how to build and refine a mapping candidate
references/validation-policy-and-reporting.md — full validation rule set, known discrepancies, and discrepancy report templates
references/runtime-validation.md — optional real-environment query validation pack

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 Dt Sec Semantic Mapping AI skill do?

Why use Dt Sec Semantic Mapping on TypingMind?

Because you install it once and use it with any model. Dt Sec Semantic Mapping 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 Dt Sec Semantic Mapping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-sec-semantic-mapping. 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 Dt Sec Semantic Mapping?

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 Dt Sec Semantic Mapping?

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

Is the Dt Sec Semantic Mapping AI skill free?

Yes. It is published on GitHub by Dynatrace 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

dt-sec-semantic-mapping

Purpose

Semantic Dictionary

Required Inputs

Baseline Sources (self-contained)

Event-Type Coverage Requirements

Workflows

Acceptable Discrepancy Policy

Scope

References

Bundled files

Frequently asked questions

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