Name: testland/locust-load-testing
Rating: 74.4 (1 reviews)
Author: testland

testland/locust-load-testing

Authors Locust load tests as Python classes - HttpUser with @task-decorated methods plus on_start hooks and between() wait_time - runs via `locust -f locustfile.py` headless mode (or distributed via `--master` / `--worker`), and exports CSV / JUnit reports for CI gating. Use when the project's primary stack is Python and the team wants load tests in the same language as the application.

Quality

93%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

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, token-efficient reference with a strong validation-gated CI workflow. The main gap is progressive disclosure: everything lives in one large SKILL.md with no split reference files, where some material (flag reference, CI template) could be offloaded.

Suggestions

Move the detailed flag/construct reference tables and the full CI workflow template into separate one-level-deep files (e.g., references/FLAGS.md, references/CI.md) and link to them from SKILL.md to reduce the inline footprint.

Add a short 'Quickstart' pointer near the top that routes readers to the locustfile example for authoring and the headless command for CI, keeping the overview lean.

Dimension	Reasoning	Score
Conciseness	The body is dense and lean — flag/construct tables, executable snippets, and an anti-pattern table carry the content with no padding or explanation of basic concepts Claude already knows (e.g., it does not explain what load testing is), matching the 'lean and efficient; every token earns its place' anchor.	3 / 3
Actionability	Guidance is fully executable and copy-paste ready: a complete minimal locustfile, `locust --headless` invocations, a full GitHub Actions workflow, and a runnable Python threshold-gate script that parses results_stats.csv — satisfying the top anchor.	3 / 3
Workflow Clarity	The CI integration is a clearly sequenced workflow with explicit validation checkpoints and feedback loops: the custom Python gate fails the build on p95 > 500ms with `::error::` and `sys.exit(1)`, complemented by `--exit-code-on-error 1` and `if: always()` artifact upload, matching the 'clear sequence with explicit validation steps; feedback loops for error recovery' anchor.	3 / 3
Progressive Disclosure	Sections are well organized, but the skill is a single ~275-line file with no bundle files (no references/ scripts/ assets/) and only one external URL reference; substantial inline reference material (full flag tables, the CI workflow template, anti-patterns) could be split into one-level-deep files, so it sits at 'some structure but content that should be separate is inline' rather than the top anchor.	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, specific description with an explicit 'Use when' trigger and a clear Python/Locust niche. Its one weakness is the 'JUnit reports' capability claim, which is not supported by the skill body (CSV + HTML only) and reads as an over-claim.

Suggestions

Remove or correct 'JUnit reports' from the description — Locust's documented outputs are CSV and HTML; claiming JUnit export overstates a capability the skill body does not show.

Keep the 'Use when ...' clause but consider also naming 'performance testing' / 'stress testing' as trigger variations users may say in addition to 'load tests'.

Dimension	Reasoning	Score
Specificity	Quotes many concrete actions ('HttpUser with @task-decorated methods plus on_start hooks and between() wait_time', 'runs via `locust -f locustfile.py` headless mode', 'distributed via `--master` / `--worker`'), but the claim 'exports CSV / JUnit reports for CI gating' is an over-claim — the skill body documents only CSV and HTML reports, not JUnit — so per the over-claim guideline it sits at the 'names actions but not comprehensive/accurate' level rather than the top anchor.	2 / 3
Completeness	Explicitly answers both what ('Authors Locust load tests as Python classes ... exports ... reports for CI gating') and when ('Use when the project's primary stack is Python and the team wants load tests in the same language as the application'), satisfying the top anchor.	3 / 3
Trigger Term Quality	Natural terms a user would say are well covered: 'Locust load tests', 'load tests', 'load testing', 'Python', and 'CI gating', matching the 'good coverage of natural terms' anchor; not merely jargon.	3 / 3
Distinctiveness Conflict Risk	The Python + Locust + CI-gating niche is specific and unlikely to trigger for the wrong skill; the body further disambiguates against k6/gatling/jmeter, fitting the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	11 / 12 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

6 days ago

Table of Contents

Discovery Implementation Validation