celery-expert

Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.

Quality

88%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

77%

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

This is a comprehensive and highly actionable Celery skill with excellent executable code examples and clear workflow guidance. The main weaknesses are moderate verbosity in introductory sections that explain concepts Claude already knows, and the monolithic structure that could benefit from progressive disclosure through separate reference files. The security patterns, performance optimizations, and TDD approach are particularly strong.

Suggestions

Remove or significantly condense the 'Core Responsibilities' section (Section 4) as it largely duplicates the overview and patterns already demonstrated in code

Split into multiple files: keep SKILL.md as a quick-start overview, move detailed patterns to PATTERNS.md, security to SECURITY.md, and testing to TESTING.md with clear cross-references

Remove the expertise bullet list in Section 1 - the code examples already demonstrate this knowledge without needing to state it

Dimension	Reasoning	Score
Conciseness	The skill contains some unnecessary verbosity, particularly in the overview section listing expertise areas Claude already knows. The 'Core Principles' and 'Core Responsibilities' sections repeat concepts. However, the code examples are lean and the patterns are well-focused.	2 / 3
Actionability	Excellent executable code examples throughout - complete task definitions, test fixtures, configuration snippets, and workflow patterns are all copy-paste ready. The patterns show both bad and good approaches with concrete implementations.	3 / 3
Workflow Clarity	Clear TDD workflow with explicit steps (write failing test → implement → refactor → verify). The pre-implementation checklist provides explicit validation checkpoints across phases. The test-first approach inherently includes verification loops.	3 / 3
Progressive Disclosure	Content is well-organized with clear sections and headers, but it's a monolithic document (~500 lines) that could benefit from splitting into separate files (e.g., PATTERNS.md, SECURITY.md, TESTING.md). No external file references are provided for deeper dives.	2 / 3
	Total	10 / 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.

This is a strong skill description that excels across all dimensions. It provides comprehensive specific capabilities, includes excellent trigger terms that users would naturally use, has an explicit 'Use when' clause with clear scenarios, and is highly distinctive to the Celery ecosystem. The only minor issue is the use of 'Expert...engineer specializing' framing which is slightly persona-like, but it uses third person throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions and capabilities: 'async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, production monitoring' plus specific patterns like 'chains, groups, chords, retries, rate limiting, Flower monitoring'.	3 / 3
Completeness	Clearly answers both what (Celery task queue engineering with specific capabilities) AND when with explicit 'Use when' clause covering four distinct scenarios: 'designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance'.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'Celery', 'distributed task queue', 'async task', 'background job', 'Redis', 'RabbitMQ', 'Celery Beat', 'scheduling', 'Flower', 'workflow orchestration', 'task queue'.	3 / 3
Distinctiveness Conflict Risk	Very clear niche focused specifically on Celery and distributed task queues with distinct technology markers (Redis/RabbitMQ, Celery Beat, Flower) that would not conflict with general Python or web development skills.	3 / 3
	Total	12 / 12 Passed

Validation

75%

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 — 12 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (631 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata' field is not a dictionary	Warning
license_field	'license' field is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	12 / 16 Passed

Repository: martinholovsky/claude-skills-generator
Commit: 1086ef2

Reviewed: 5 months ago

Table of Contents

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