projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
Install with Tessl CLI
npx tessl i github:wshobson/agents --skill projection-patternsOverall
score
78%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillEvaluation — 95%
↑ 1.73xAgent success when using this skill
Validation for skill structure
Discovery
90%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 well-crafted skill description with strong completeness and distinctiveness. It clearly identifies both what the skill does and when to use it, with domain-specific terminology that creates clear differentiation. The main weakness is that the 'what' portion could be more specific about the concrete actions performed.
Suggestions
Expand the capabilities list with more specific actions like 'aggregate event data, denormalize schemas, handle eventual consistency, rebuild projections from event history'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (event streams, CQRS) and some actions ('build read models and projections'), but doesn't list multiple concrete specific actions like 'create materialized views, aggregate event data, denormalize for queries'. | 2 / 3 |
Completeness | Clearly answers both what ('Build read models and projections from event streams') AND when ('Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'read models', 'projections', 'event streams', 'CQRS', 'read sides', 'materialized views', 'query performance', 'event-sourced systems' - these are terms developers naturally use when working in this domain. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on CQRS/event-sourcing read side patterns. The combination of 'read models', 'projections', 'event streams', and 'CQRS' creates a distinct trigger profile unlikely to conflict with general database or API skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides highly actionable, executable code templates for building event-sourced projections with good coverage of common patterns (basic projector, aggregations, search, multi-table). However, it's verbose with repetitive handler implementations and lacks explicit validation/error recovery workflows that are critical for projection rebuilds and failure scenarios. The content would benefit from being split across multiple files with a leaner overview.
Suggestions
Add explicit validation and error recovery workflow for projection rebuilds (e.g., verify checkpoint integrity, handle partial failures, validate read model state after rebuild)
Condense repetitive handler methods by showing one complete example and noting the pattern for others, or extract to a separate EXAMPLES.md file
Remove the 'When to Use This Skill' section and ASCII diagram - Claude can infer appropriate use cases from the content itself
Add a concrete error handling template showing how to handle and recover from projection failures mid-batch
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary elements like the ASCII diagram and the 'When to Use This Skill' section that Claude could infer. The templates are thorough but could be more condensed - multiple similar handler methods are shown in full when patterns could be established more efficiently. | 2 / 3 |
Actionability | Provides fully executable Python code with complete class implementations, SQL queries, and Elasticsearch operations. All templates are copy-paste ready with proper imports, type hints, and realistic database operations. | 3 / 3 |
Workflow Clarity | The Projector class shows the run/rebuild workflow, but lacks explicit validation checkpoints for critical operations like rebuilding projections or handling failures. The 'Best Practices' section mentions error handling but doesn't show concrete validation steps or recovery procedures. | 2 / 3 |
Progressive Disclosure | Content is reasonably organized with templates and best practices sections, but the skill is monolithic at ~400 lines. The templates could be split into separate files with the main skill providing an overview. External resources are linked but internal content organization could be improved. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata' field is not a dictionary | Warning |
license_field | 'license' field is missing | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 13 / 16 Passed | |
- Reviewed
Table of Contents
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