django-celery

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.

Quality

96%

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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 strong, production-grade reference with executable code, clear sequencing, and verification-oriented patterns across the full Celery lifecycle. The main gap is progressive disclosure: everything lives in one large file rather than splitting detailed reference material into bundle files.

Suggestions

Split the lengthy Canvas, Database-Defined Beat Schedule, and Monitoring sections into separate reference files (e.g. references/canvas.md, references/beat.md) linked from the body, keeping SKILL.md as a concise overview.

Add an explicit validate-fix-retry feedback loop for the destructive payment/charge workflows to make the existing idempotency guards into a clear checkpoint sequence.

Move the debug_task example and a few redundant inline comments out of the core setup section to tighten the entrypoint walkthrough.

Dimension	Reasoning	Score
Conciseness	The body is code-forward with terse, purposeful comments and almost no concept-explanation padding; it assumes Claude's competence throughout and avoids verbosity. A couple of inline comments could be trimmed but the content stays lean.	3 / 3
Actionability	Every section provides fully executable, copy-paste-ready code and commands — install line, real celery.py/settings, runnable worker/beat commands, and concrete task definitions with real kwargs.	3 / 3
Workflow Clarity	Sections progress logically from setup through design, calling, scheduling, advanced patterns, testing, monitoring, and a production checklist; idempotency guards, the DLQ pattern, monitoring commands, and test sections provide verification checkpoints and error-recovery guidance.	3 / 3
Progressive Disclosure	Content is well-organized into clear sections but it is a single inline ~450-line file with no bundle references; material such as the full canvas reference, DB-defined beat scheduling, and monitoring guide could be split into separate reference files.	2 / 3
	Total	11 / 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 specific, trigger-rich, and complete, naming concrete capabilities alongside explicit use-when guidance scoped tightly to Django + Celery. It is concise with no fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Enumerates seven concrete capability areas — 'configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing' — matching the anchor for multiple specific concrete actions.	3 / 3
Completeness	Explicitly answers both 'what' (the enumerated task patterns) and 'when' (the 'Use when...' trigger clause), satisfying the highest anchor.	3 / 3
Trigger Term Quality	The 'Use when adding background jobs, scheduled tasks, or async processing to a Django app' clause uses natural phrasing a user would actually say, covering several common variations.	3 / 3
Distinctiveness Conflict Risk	'Django + Celery' is a clearly narrow niche with distinct triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 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
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: affaan-m/ECC
Path: skills/django-celery/SKILL.md
Commit: 754b8dd

Reviewed: 2 days ago

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