dt-app-notebooks
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, visualizations, markdown documentation, and analytics workflows. Supports notebook creation from scratch, section-based updates, data extraction from Document Store, structure analysis, investigation workflows, and collaborative documentation.
71
58%
Does it follow best practices?
Impact
89%
1.28xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/dt-app-notebooks/SKILL.mdQuality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong description with excellent specificity and domain-specific trigger terms that clearly carve out a distinct niche around Dynatrace notebooks. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill over others. Adding trigger guidance would elevate this from good to excellent.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about Dynatrace notebooks, DQL queries, notebook JSON, or building analytics dashboards in Dynatrace.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: create, modify, query, analyze notebook JSON, section-based updates, DQL queries, visualizations, markdown documentation, analytics workflows, data extraction from Document Store, structure analysis, investigation workflows, and collaborative documentation. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with detailed capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The when is only implied through the listed capabilities, which caps this at 2 per the rubric guidelines. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Dynatrace', 'notebooks', 'DQL queries', 'visualizations', 'markdown', 'Document Store', 'analytics workflows', 'notebook JSON', 'sections'. These cover the domain-specific terms a user working with Dynatrace would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific 'Dynatrace notebooks' domain, DQL queries, and Document Store references. Very unlikely to conflict with other skills given the narrow, well-defined niche. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides reasonable structural organization and a clear progressive disclosure strategy with reference files, but it critically lacks actionable, executable content in the main SKILL.md itself. Nearly all concrete guidance (creation workflows, validation commands, modification patterns) is deferred to reference files, leaving the skill body as mostly descriptive metadata about notebooks rather than instructions Claude can act on. The conciseness is moderate—some sections could be tightened, but the bigger issue is the absence of concrete examples and commands.
Suggestions
Add at least one complete, executable example for the most common workflow (e.g., creating a minimal notebook with a markdown section and a DQL section) directly in SKILL.md rather than deferring everything to references.
Include the actual schema validation command and a concrete jq extraction example so Claude can act without loading reference files for basic tasks.
Add a concrete modification example showing how to add a section to an existing notebook, with before/after JSON snippets.
Trim the 'When to use this skill' and 'Four main workflows' sections—these are descriptive rather than instructive and consume tokens without adding actionable value.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary explanation (e.g., the 'When to use this skill' list, the 'Four main workflows' enumeration, and the progressive disclosure explanation section are somewhat verbose). However, it generally avoids explaining concepts Claude already knows and keeps most sections reasonably tight. | 2 / 3 |
Actionability | The skill lacks concrete, executable code or commands. There are no copy-paste ready examples for creating, modifying, or querying notebooks. The JSON structure example is illustrative but not actionable. All actual workflows are deferred to reference files that aren't provided, and the skill itself contains no executable guidance—just descriptions and pointers. | 1 / 3 |
Workflow Clarity | The skill lists mandatory steps (load reference, validate queries, validate schema) but the actual workflow sequences are entirely deferred to reference files. The numbered mandatory steps provide some structure, but there are no validation checkpoints with concrete commands in the skill itself, and the feedback loops are absent at this level. | 2 / 3 |
Progressive Disclosure | The skill has a clear reference table and uses '→' arrows to signal when to load references, which is good structure. However, since no bundle files were provided, we cannot verify the references exist. Additionally, the skill defers almost ALL actionable content to references, making the SKILL.md itself too thin to be useful standalone—it's more of a table of contents than a quick-start guide. | 2 / 3 |
Total | 7 / 12 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- Dynatrace/dynatrace-for-ai
- Commit
4991356
- Reviewed
Table of Contents
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