dt-obs-log-semantic-mapping
SkillMonitoring & opsSuggest 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).
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the dt-obs-log-semantic-mapping skill
What this skill tells your AI
The instructions your AI receives, as published by dynatrace/dynatrace-for-ai in skills/dt-obs-log-semantic-mapping/SKILL.md and read by ahel’s review.
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 logsfields (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
| Class | Description | Key namespaces | Example sources |
|---|---|---|---|
authentication | Login, logout, MFA, token | audit.*, actor.*, browser.*, device.* | CyberArk, Okta, Azure SignInLogs |
authorization | Access decisions, permission changes | audit.*, actor.*, object.* | CyberArk, Okta |
user_action | CRUD on platform resources | audit.*, actor.*, object.*, product.* | Okta, GitHub, Sonatype |
http | HTTP request/response (WAF, network devices) | http.*, url.*, server.*, geo.*, client.* | Akamai SIEM, Cloudflare |
Workflows
| Mode | Input | Source |
|---|---|---|
| Workflow A — Suggest mapping | Raw vendor log payload | references/mapping-workflow.md § Workflow A |
| Workflow B1 — Static validation | Pasted ingested log event | references/mapping-workflow.md § Workflow B1 |
| Workflow B2 — Runtime validation | log.source value + live tenant access | references/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-essentialsskill 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 discrepanciesreferences/mapping-workflow.md— Intake checklist, Workflow A and B1 procedures, content field analysis, field priority orderreferences/validation-rules.md— Required fields, content/enum/type rules, discrepancy severityreferences/openpipeline-constraints.md— OpenPipeline processor command/function/operator/matcher restrictions;parseJsonunavailability +parse→fieldsFlattenalternative; iterative operators for array castingreferences/report-format.md— Mapping table, diff table, OpenPipeline sketch, Validation Summary templatesreferences/runtime-validation.md— Workflow B2: fetch live records, then run B1samples/audit-logs.json— Mapped samples: CyberArk, Okta, Azure SignInLogs, Sonatype, GitHubsamples/http-logs.json— Mapped samples: Akamai SIEM (WAF/HTTP class)- Dynatrace Log Semantic Dictionary
Signals
- GitHub stars
- 142
- Forks
- 29
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
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dt-obs-log-semantic-mapping- Source
- github.com/dynatrace/dynatrace-for-ai