Dt Obs Log Semantic Mapping logo

Dt Obs Log Semantic Mapping

Organization
Dynatrace
dt-obs-log-semantic-mapping

Suggest and validate semantic dictionary (SD) mappings for audit log integrations using raw vendor log payloads or live ingested events. Use when: mapping a vendor audit log feed, authentication logs, user activity logs to the Dynatrace SD; checking required semantic fields; proposing OpenPipeline processor extraction rules based on DQL; running runtime validation (fetches live logs by log.source, then applies static validation).

Overview

PublisherDynatrace
Repositorydynatrace-for-ai
Skill namedt-obs-log-semantic-mapping
Stars
156
Forks
30
Bundled files
8
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 8 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 Obs Log Semantic Mapping 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.
  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/Dynatrace/dynatrace-for-ai.git /tmp/dynatrace-for-ai
mkdir -p .claude/skills
cp -r /tmp/dynatrace-for-ai/skills/dt-obs-log-semantic-mapping .claude/skills/dt-obs-log-semantic-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dt Obs Log 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 Obs Log 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 Obs Log 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-obs-log-semantic-mapping

Build and validate semantic-dictionary-aligned mappings for audit log integrations.

Purpose

Use this skill when a user wants to:

  • Suggest a mapping from a raw vendor audit log payload to Dynatrace fetch logs fields (Workflow A).
  • Validate a mapping against a pasted ingested log event (Workflow B1 — static).
  • Validate against live tenant data via live tenant access (Workflow B2 — runtime: fetches logs by log.source, then runs B1 on the result).

Log Classes

ClassDescriptionKey namespacesExample sources
authenticationLogin, logout, MFA, tokenaudit.*, actor.*, browser.*, device.*CyberArk, Okta, Azure SignInLogs
authorizationAccess decisions, permission changesaudit.*, actor.*, object.*CyberArk, Okta
user_actionCRUD on platform resourcesaudit.*, actor.*, object.*, product.*Okta, GitHub, Sonatype
httpHTTP request/response (WAF, network devices)http.*, url.*, server.*, geo.*, client.*Akamai SIEM, Cloudflare

Workflows

ModeInputSource
Workflow A — Suggest mappingRaw vendor log payloadreferences/mapping-workflow.md § Workflow A
Workflow B1 — Static validationPasted ingested log eventreferences/mapping-workflow.md § Workflow B1
Workflow B2 — Runtime validationlog.source value + live tenant accessreferences/runtime-validation.md — fetches logs, then runs B1

Key Concepts

Content field burial: The primary validation concern. Fields in content (the raw vendor payload) that could be promoted to top-level semantic attributes but are not. The skill always inventories buried vs promoted fields and proposes OpenPipeline extraction rules to fix gaps.

Prerequisite: When proposing OpenPipeline processor extraction rules, load the dt-dql-essentials skill first. OpenPipeline processors use DQL functions (parse, fieldsAdd, splitString, etc.) — using non-DQL syntax produces invalid rules.

Sparse mappings are valid: Integrations like GitHub or Sonatype may only populate core fields. Minimum required: timestamp, log.source, content, loglevel, audit.action, audit.identity.

References

  • references/data-model-notes.md — Log SD field taxonomy, audit namespace, enums, sample-derived patterns and known discrepancies
  • references/mapping-workflow.md — Intake checklist, Workflow A and B1 procedures, content field analysis, field priority order
  • references/validation-rules.md — Required fields, content/enum/type rules, discrepancy severity
  • references/openpipeline-constraints.md — OpenPipeline processor command/function/operator/matcher restrictions; parseJson unavailability + parse→fieldsFlatten alternative; iterative operators for array casting
  • references/report-format.md — Mapping table, diff table, OpenPipeline sketch, Validation Summary templates
  • references/runtime-validation.md — Workflow B2: fetch live records, then run B1
  • samples/audit-logs.json — Mapped samples: CyberArk, Okta, Azure SignInLogs, Sonatype, GitHub
  • samples/http-logs.json — Mapped samples: Akamai SIEM (WAF/HTTP class)
  • Dynatrace Log Semantic Dictionary

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 Obs Log Semantic Mapping AI skill do?

Suggest and validate semantic dictionary (SD) mappings for audit log integrations using raw vendor log payloads or live ingested events. Use when: mapping a vendor audit log feed, authentication logs, user activity logs to the Dynatrace SD; checking required semantic fields; proposing OpenPipeline processor extraction rules based on DQL; running runtime validation (fetches live logs by log.source, then applies static validation).

Why use Dt Obs Log Semantic Mapping on TypingMind?

Because you install it once and use it with any model. Dt Obs Log 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 Obs Log 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-obs-log-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 Obs Log 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 Obs Log Semantic Mapping?

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

Is the Dt Obs Log 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.

Related AI Skills

View all
JuliusBrussee logo

Caveman Discover

JuliusBrussee
CommunityPopular

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

6.2K
106.3K
Instructions
View
JuliusBrussee logo

Caveman Manage

JuliusBrussee
CommunityPopular

Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.

6.2K
106.3K
Instructions
View
JuliusBrussee logo

Caveman Setup

JuliusBrussee
CommunityPopular

Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.

6.2K
106.3K
Instructions
View
nexu-io logo

Eng Runbook

nexu-io
OrganizationPopular

An engineering runbook — service overview, alerts table, dashboards links, common procedures with copy-pasteable commands, on-call rotation, and an incident-response checklist. Use when the brief…

11.2K
96.7K
1 files
View

Set up your own AI workspace now

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