Name: load-testing
Rating: 73.6 (1 reviews)
Author: databricks

load-testing

Load test a Databricks App to find its maximum QPS. Use when: (1) User says 'load test', 'benchmark', 'QPS', 'throughput', or 'performance test', (2) User wants to find how many queries per second their app can handle, (3) User wants to set up load testing scripts for their agent, (4) User wants to view load test results/dashboard.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

92%

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

A highly actionable, well-sequenced skill with concrete code and validation checkpoints. Its main gap is progressive disclosure: it is monolithic and references a bundle file that is not present.

Suggestions

Move the large inline detail (deploy YAML, dashboard internals, full parameter table) into reference files under references/ and link to them from SKILL.md to reduce the monolithic body.

Either ship examples/mock_openai_client.py or remove the broken reference to it, since the file does not exist in the bundle.

Consider extracting the mock-client setup into a dedicated reference so the main flow stays a lean overview.

Dimension	Reasoning	Score
Conciseness	The body is long but dense with actionable content — full configs, CLI examples, and parameter tables — and avoids explaining concepts Claude already knows (e.g., what Locust or load testing is); nearly every token earns its place.	3 / 3
Actionability	It provides copy-paste-ready material throughout: a complete pyproject.toml, a deploy bundle YAML, concrete CLI invocations, and a full parameter reference table.	3 / 3
Workflow Clarity	Steps are clearly sequenced (gather params → Steps 1–5 → Troubleshooting) with explicit checkpoints such as verifying apps are ACTIVE before proceeding and a healthcheck/warmup before each test run.	3 / 3
Progressive Disclosure	The skill is a single monolithic SKILL.md with all detail inline and no real bundle files (references/scripts/assets are absent); the one external reference, examples/mock_openai_client.py, does not exist, so content is not appropriately split across files.	2 / 3
	Total	11 / 12 Passed

Description

90%

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

A strong, well-targeted description with explicit triggers and a clear niche. Its only weakness is that it names a single action rather than enumerating multiple concrete capabilities.

Dimension	Reasoning	Score
Specificity	It names the domain and a concrete outcome ("Load test a Databricks App to find its maximum QPS") but describes a single action rather than listing multiple specific concrete actions, so it stops short of the top anchor.	2 / 3
Completeness	It explicitly answers both what ("Load test a Databricks App to find its maximum QPS") and when via a numbered "Use when:" list with four explicit triggers.	3 / 3
Trigger Term Quality	The "Use when" clause lists natural terms a user would actually say — 'load test', 'benchmark', 'QPS', 'throughput', 'performance test' — giving good coverage of common phrasings.	3 / 3
Distinctiveness Conflict Risk	The narrow niche (Databricks App load testing for QPS) with distinctive triggers like 'QPS' and 'throughput' makes it unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 1 missing	Warning

	Total	15 / 16 Passed

Repository: databricks/app-templates
Commit: 2a4c792

Reviewed: 5 days ago

Table of Contents

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