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testland/celery-tests

Authors and runs Celery task tests in Python - `pytest-celery` fixtures (`celery_app`, `celery_worker` per-test, `celery_session_worker` per-session); `task_always_eager` config NOT recommended for unit tests; `apply()` for synchronous test invocation; mock-and-patch retry patterns via `unittest.mock.patch` on `task.retry`. Use when the user works with Celery task workers and needs unit / integration tests across function-style or class-style tasks.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

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Overview
Quality
Evals
Security
Files

Quality

Content

85%

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

The body is highly actionable and well-structured with a clear sequenced workflow and clean sectioning. The only weakness is mild redundancy where the worked example re-implements code from the step sections.

Suggestions

Collapse the 'Worked example' section so it references Steps 2 and 3 rather than reprinting near-identical success/retry test code, or remove it if the step examples already suffice.

Trim repeated verbatim blockquotes from the Celery docs to a single cited reason per point (e.g. the task_always_eager warning appears in both Step 1 and the Anti-patterns table).

DimensionReasoningScore

Conciseness

Mostly code-forward and lean, but the 'Worked example' section duplicates the success/retry test code already shown in Steps 2 and 3, and verbatim doc blockquotes recur; tightening the overlap would earn its tokens more.

2 / 3

Actionability

Fully executable Python and YAML throughout: copy-paste-ready pytest-celery fixture usage, apply() invocation, chord/chain/group primitives, and CI service-container config with concrete image tags and ports.

3 / 3

Workflow Clarity

A clearly sequenced 7-step 'How to use' list with cross-references to Steps 1-7, unit-vs-integration decision guidance, and a verification endpoint in the worked example ('Running pytest -v reports both tests passing').

3 / 3

Progressive Disclosure

Single-file skill with no bundle files; content is well-organized into clearly headed sections (Overview, When to use, How to use, Steps, Anti-patterns, Limitations, References) and only one-level-deep external URL references, none nested.

3 / 3

Total

11

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12

Passed

Description

100%

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, and a distinctive Celery-testing niche. It avoids vague fluff while staying dense but readable.

DimensionReasoningScore

Specificity

Lists multiple concrete actions: 'Authors and runs Celery task tests', fixtures enumerated ('celery_app', 'celery_worker' per-test, 'celery_session_worker' per-session), 'apply() for synchronous test invocation', and 'mock-and-patch retry patterns via unittest.mock.patch on task.retry'.

3 / 3

Completeness

Explicitly answers both what ('Authors and runs Celery task tests in Python...') and when ('Use when the user works with Celery task workers and needs unit / integration tests...'), satisfying the 'Use when' trigger requirement.

3 / 3

Trigger Term Quality

Natural terms a Celery developer would say are well covered: 'Celery task tests', 'unit / integration tests', 'Celery task workers', 'function-style or class-style tasks', plus the explicit 'Use when...' trigger clause.

3 / 3

Distinctiveness Conflict Risk

Narrow niche (Celery task testing specifically) with distinct triggers; the reference to sister tools (sidekiq-tests, bullmq-tests) reinforces a clear, non-overlapping scope unlikely to fire for unrelated skills.

3 / 3

Total

12

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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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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