cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
75
58%
Does it follow best practices?
Impact
84%
1.33xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/backend-development/skills/cqrs-implementation/SKILL.mdQuality
Discovery
75%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 well-structured with a clear 'Use when' clause that covers the main trigger scenarios, and it occupies a distinct niche around CQRS. However, it could be more specific about the concrete actions it performs (e.g., creating command handlers, building read projections, setting up event stores) and could include more natural trigger term variations that users might use when requesting help with this pattern.
Suggestions
Add more concrete actions such as 'create command handlers, build read projections, define event stores, set up message buses' to improve specificity.
Include additional natural trigger terms like 'command handler', 'read projection', 'event store', 'separate read database', and spell out 'Command Query Responsibility Segregation' alongside the acronym for better keyword coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (CQRS) and mentions some actions like 'separating read and write models' and 'optimizing query performance', but doesn't list multiple concrete specific actions (e.g., creating command handlers, building read projections, defining event stores). | 2 / 3 |
Completeness | Clearly answers both 'what' (implement CQRS for scalable architectures) and 'when' (explicit 'Use when' clause covering separating read/write models, optimizing query performance, or building event-sourced systems). | 3 / 3 |
Trigger Term Quality | Includes relevant terms like 'CQRS', 'read and write models', 'query performance', and 'event-sourced systems', but misses common natural variations users might say such as 'command handler', 'read model projection', 'event store', 'separate database for reads', or the full acronym expansion alongside the abbreviation. | 2 / 3 |
Distinctiveness Conflict Risk | CQRS is a well-defined architectural pattern with distinct terminology; the description's focus on read/write model separation and event sourcing creates a clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable Python code for CQRS implementation, which is its primary strength. However, it is excessively verbose—explaining concepts Claude already understands, including a large architecture diagram and component table, and inlining five substantial code templates that should be split across referenced files. The lack of an explicit implementation workflow with validation checkpoints and the monolithic structure significantly reduce its effectiveness as a skill file.
Suggestions
Remove the 'Core Concepts' section (architecture diagram, component table) entirely—Claude already understands CQRS concepts; focus only on implementation-specific guidance.
Split the five templates into separate referenced files (e.g., COMMANDS.md, QUERIES.md, FASTAPI_INTEGRATION.md, SYNC.md) and keep SKILL.md as a concise overview with navigation links.
Add an explicit step-by-step implementation workflow with validation checkpoints, e.g.: 1) Define commands/events, 2) Implement command handlers, 3) Set up event store, 4) Build projections, 5) Verify read model sync with a test query.
Trim the 'When to Use This Skill' list—this duplicates the skill description and wastes tokens on decision criteria Claude can infer.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. The architecture diagram, component table, and 'When to Use This Skill' section explain concepts Claude already knows. Five large templates with extensive boilerplate code could be significantly condensed. The 'Core Concepts' section is entirely unnecessary for Claude. | 1 / 3 |
Actionability | The code templates are concrete, executable Python with real implementations including FastAPI endpoints, command/query buses, event store integration, and read model synchronization. Code is copy-paste ready with proper imports and type hints. | 3 / 3 |
Workflow Clarity | The templates show individual components clearly but lack an explicit step-by-step workflow for implementing CQRS end-to-end. There's no validation checkpoint for verifying the read model sync is working correctly, and no explicit sequence for wiring components together. The read model rebuild has some error handling but no verification step. | 2 / 3 |
Progressive Disclosure | All content is inlined in a single monolithic file with no references to external files. Five large code templates, an architecture diagram, a component table, and best practices are all crammed together. The templates for event store, projections, and consistency handling should be split into separate reference files. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (550 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- wshobson/agents
- Commit
70444e5
- Reviewed
Table of Contents
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