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dt-app-notebooks

Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.

71

1.22x
Quality

70%

Does it follow best practices?

Impact

86%

1.22x

Average score across 2 eval scenarios

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./skills/dt-app-notebooks/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

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

This is a well-structured skill that efficiently communicates the notebook JSON format, key constraints, and visualization options while appropriately delegating detailed workflows to reference files. Its main weakness is that the core create/update workflow — the most critical multi-step process — is entirely deferred to a reference file, leaving the SKILL.md without an inline sequenced workflow with validation checkpoints. The conciseness and progressive disclosure are strong.

Suggestions

Include a condensed numbered workflow (3-5 steps) for create/update inline in the SKILL.md with explicit validation checkpoints, rather than deferring entirely to create-update.md — the reference can still hold the full details.

Add a minimal end-to-end example showing the complete flow from creating a notebook JSON to deploying it with `dtctl apply`, making the skill more immediately actionable without loading references.

DimensionReasoningScore

Conciseness

The content is lean and efficient. It doesn't explain what Dynatrace is or what notebooks conceptually are — it jumps straight into the JSON structure and actionable rules. Every section earns its place, and the visualization type listing is a useful reference table rather than verbose explanation.

3 / 3

Actionability

The JSON structure example is concrete and copy-paste ready, and the key rules provide specific commands (e.g., `dtctl get notebook <id> -o json --plain > notebook.json`). However, the actual create/update workflow is deferred entirely to a reference file, and there's no inline executable example of creating or deploying a notebook end-to-end. The guidance is a mix of concrete and delegated.

2 / 3

Workflow Clarity

The skill mentions a mandatory order workflow but delegates the full steps to `references/create-update.md`. Key rules are listed (download first, validate, deploy with dtctl apply) but the actual sequenced steps with validation checkpoints are not present in the SKILL.md itself. The update workflow has good safety guidance (always download first, never reconstruct) but lacks an explicit numbered sequence with feedback loops inline.

2 / 3

Progressive Disclosure

The content is well-structured with a clear overview, inline essentials (JSON structure, key rules, visualization types), and a well-organized reference table with three clearly signaled one-level-deep references. The 'When to Load' column in the reference table is an excellent touch for navigation. References are appropriately scoped.

3 / 3

Total

10

/

12

Passed

Description

67%

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 strong in specificity and distinctiveness, clearly identifying the Dynatrace notebook domain with concrete actions and specific sub-components like DQL queries. However, it lacks an explicit 'Use when...' clause which caps completeness, and could benefit from additional natural trigger terms users might use when requesting help with Dynatrace notebooks.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about Dynatrace notebooks, DQL queries, notebook JSON structure, or Dynatrace visualizations.'

Include additional natural trigger terms like 'Dynatrace Query Language', 'monitoring notebooks', 'observability', or file extensions to improve keyword coverage.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: create, modify, query, and analyze notebook JSON. Also specifies sub-components: sections, DQL queries, and visualizations.

3 / 3

Completeness

Clearly answers 'what does this do' (create, modify, query, analyze notebook JSON with sections, DQL queries, visualizations), but lacks an explicit 'Use when...' clause specifying when Claude should select this skill.

2 / 3

Trigger Term Quality

Includes good terms like 'Dynatrace', 'notebooks', 'DQL queries', and 'visualizations', but misses common user variations like '.json', 'notebook API', 'Dynatrace Query Language', or 'dashboard'. Could also mention 'monitoring' or 'observability' as contextual triggers.

2 / 3

Distinctiveness Conflict Risk

Very distinct niche — 'Dynatrace notebooks' with 'DQL queries' is highly specific and unlikely to conflict with other skills. The combination of Dynatrace + notebooks + DQL creates a clear, unique trigger profile.

3 / 3

Total

10

/

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
Dynatrace/dynatrace-for-ai
Reviewed

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