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dt-obs-analytics

Analyze dashboards and notebooks using Davis analyzers — anomaly detection, novelty scoring, and correlation. Use when the user references a specific Dynatrace dashboard or notebook (by URL, UUID, or name) and asks what it shows, which DQL queries it runs, whether a tile looks off, or wants to find anomalies, score novelty, or correlate its metrics. The trigger is a dashboard or notebook as the data source, not a general DQL question. This skill extracts timeseries queries efficiently without reading the full raw document JSON, then optionally runs Davis analyzers on the extracted metrics. Trigger phrases: "what's wrong on this dashboard", "analyze this notebook", "find anomalies", "novelty score", "correlate metrics", "extract DQL from dashboard", "dashboard URL", "tile", "run-analyzer", "timeseries extraction", "Davis analyzer".

75

Quality

93%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a lean, highly actionable operational guide that keeps implementation in the bundled scripts and teaches the non-obvious platform behaviors (envelope unwrapping, timeframe stripping, variable substitution, score interpretation). Minor conciseness loss from a partly redundant end-to-end section and the verify step not being an explicit numbered checkpoint keep it just short of perfect.

Suggestions

Collapse or trim the 'End-to-end' section since it recombines the Step 1 and Step 2 commands already shown; reference those steps instead of repeating the full payloads.

Promote 'Verifying a single extracted query' into an explicit numbered checkpoint within the main workflow (e.g., 'Step 3 — Verify') so validation is part of the sequenced flow rather than a detached section.

Consolidate the repeated '$(cat queryset.json)' inline-payload pattern by stating it once and referencing it from later examples, or prefer the '--data payload.json' form throughout to also dodge argument-length limits.

DimensionReasoningScore

Conciseness

The body is dense and assumes Claude's competence (no padding about what dashboards or Davis analyzers are), and every section teaches a non-obvious behavior, but the 'End-to-end' section recombines commands already shown in Steps 1–2 and the '$(cat queryset.json)' pattern repeats across several blocks, so a few tokens could be trimmed.

4 / 5

Actionability

Fully executable bash with realistic UUIDs and analyzer names, a '--data payload.json' fallback for large querysets, concrete score thresholds (≥0.7 → abnormal), and a URL-part-to-payload-field table — copy-paste ready and covering the common cases (extract, single tile, list-only, anomaly, novelty, correlation, chaining, variable substitution, verification).

5 / 5

Workflow Clarity

The pipeline is clearly sequenced (parse URL → extract → analyze → interpret) with a 3-step end-to-end example, error envelopes with recovery guidance (schema drift, analyzer availability, silent variable drops), and a 'listOnly' disambiguation gate for batch extraction; the dedicated verify step is present but not woven as an explicit numbered checkpoint inside the main flow.

4 / 5

Progressive Disclosure

A clean overview with a pipeline diagram, well-signaled one-level-deep references to three scripts (all verified to exist under scripts/), implementation kept in the bundle rather than inlined, and no nested references — easy to navigate.

5 / 5

Total

18

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: third-person voice, concrete actions, explicit 'Use when' trigger guidance with concrete phrases, and a deliberate boundary statement distinguishing it from general DQL skills. It scores at the top of every dimension.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'anomaly detection, novelty scoring, and correlation', 'extracts timeseries queries efficiently without reading the full raw document JSON', and 'runs Davis analyzers on the extracted metrics' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both what ('Analyze dashboards and notebooks using Davis analyzers … extracts timeseries queries … runs Davis analyzers') and when ('Use when the user references a specific Dynatrace dashboard or notebook … and asks what it shows, which DQL queries it runs, whether a tile looks off …') with concrete trigger phrases.

5 / 5

Trigger Term Quality

An explicit 'Trigger phrases' list gives comprehensive natural terms users would say ('what's wrong on this dashboard', 'find anomalies', 'novelty score', 'correlate metrics', 'dashboard URL', 'tile', 'Davis analyzer') plus synonyms and identifier forms ('by URL, UUID, or name').

5 / 5

Distinctiveness Conflict Risk

The explicit boundary clause — 'The trigger is a dashboard or notebook as the data source, not a general DQL question' — carves out a clear niche and minimizes overlap with generic DQL skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
Dynatrace/dynatrace-for-ai
Reviewed

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