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.
77
66%
Does it follow best practices?
Impact
94%
1.77xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/backend-development/skills/projection-patterns/SKILL.mdQuality
Discovery
89%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 solid skill description that clearly targets a specific architectural pattern (CQRS read sides in event-sourced systems) with an explicit 'Use when' clause and good trigger term coverage. Its main weakness is that the 'what' portion could enumerate more concrete actions beyond the general 'build read models and projections'. Overall it performs well for skill selection in a multi-skill environment.
Suggestions
Expand the capability list with more specific actions, e.g., 'Build read models and projections from event streams, handle projection rebuilds, create denormalized query tables, manage eventual consistency patterns.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (CQRS/event sourcing) and some actions ('build read models and projections from event streams'), but doesn't list multiple concrete actions like specific techniques (e.g., creating denormalized tables, handling projection rebuilds, managing read model schemas). | 2 / 3 |
Completeness | Clearly answers both 'what' (build read models and projections from event streams) and 'when' (implementing CQRS read sides, building materialized views, optimizing query performance in event-sourced systems) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords that a user in this domain would use: 'read models', 'projections', 'event streams', 'CQRS', 'read sides', 'materialized views', 'query performance', 'event-sourced systems'. Good coverage of the terminology a developer would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific niche targeting CQRS read sides and event-sourced projections. The combination of 'read models', 'CQRS', and 'event streams' creates a very distinct trigger profile unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 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 building event-sourced projections, which is its primary strength. However, it is severely bloated—five full template implementations with repetitive SQL patterns make it a poor use of context window. The lack of progressive disclosure (no bundle files, no content splitting) and missing error handling/validation workflows in a domain involving database writes are significant weaknesses.
Suggestions
Reduce to 1-2 core templates in SKILL.md and move the remaining templates (Elasticsearch, aggregating, multi-table) to separate bundle files like TEMPLATES.md, referenced with clear links.
Add explicit error handling and validation steps to the Projector workflow—e.g., what happens when projection.apply() fails, how to detect and recover from partial updates, and how to verify projection consistency.
Remove the 'Core Concepts' architecture diagram and projection types table—Claude understands these patterns. Replace with a brief one-line framing and jump straight to the base class template.
Consolidate repetitive SQL handler patterns: show one complete handler, then use brief diffs or bullet points for variations instead of repeating the full async with/conn.execute pattern dozens of times.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. The 'Core Concepts' section explains projection types and architecture that Claude already understands. Templates 2-5 are highly repetitive variations of the same pattern (event handler dispatching to SQL updates), with massive amounts of boilerplate SQL that could be condensed to one example plus brief notes on variations. | 1 / 3 |
Actionability | All templates provide fully executable Python code with concrete SQL queries, proper async patterns, and real library usage (asyncpg, elasticsearch). The code is copy-paste ready with realistic event handling, database operations, and clear data structures. | 3 / 3 |
Workflow Clarity | The Projector class shows a run loop and rebuild method, giving some workflow structure. However, there are no explicit validation checkpoints, no error handling in the projection pipeline, and the rebuild process lacks verification steps. For a system involving database writes and event processing, missing error recovery and validation caps this at 2. | 2 / 3 |
Progressive Disclosure | Everything is in a single monolithic file with no bundle files. The massive amount of template code (5 full projection implementations) should be split into separate reference files. The skill would benefit enormously from a concise overview pointing to detailed templates in separate files. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- Dicklesworthstone/pi_agent_rust
- Commit
99da384
- Reviewed
Table of Contents
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