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.
87
88%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
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 |
Implementation
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 |
Validation
75%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
Table of Contents
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