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recommendation-system

Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.

63

Quality

75%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Fix and improve this skill with Tessl

tessl review fix ./plugins/recommendation-system/skills/recommendation-system/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

The skill is highly actionable with executable, concrete code examples covering the full recommendation system stack. However, it is excessively verbose with significant redundancy across sections (caching, monitoring, and recommendation service logic each appear multiple times). The content would benefit greatly from moving detailed implementations into the referenced files and keeping SKILL.md as a lean overview with the Quick Start and brief pointers.

Suggestions

Reduce redundancy by removing duplicate code patterns — the RecommendationService, monitoring, and caching code each appear 2-3 times across Quick Start, Core Components, Known Issues, and Common Patterns sections.

Move the detailed Known Issues section (cold start, cache invalidation, thundering herd, etc.) and Common Patterns into the referenced files (e.g., references/caching-strategies.md, references/production-architecture.md) to keep SKILL.md lean.

Add validation checkpoints to the Quick Start workflow: verify Redis is running (redis-cli ping), verify the API starts successfully, and check the curl response matches expected output.

Remove the 'When to Use This Skill' section — this duplicates the YAML frontmatter description and is not actionable guidance.

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~350+ lines. There is significant redundancy: the RecommendationService pattern is essentially shown three times (Quick Start, Core Components, Common Patterns). The monitoring code appears twice. The caching logic appears in multiple sections. Many concepts (caching, A/B testing, feature stores) are explained at a level Claude already understands.

1 / 3

Actionability

The skill provides fully executable code throughout — the Quick Start is copy-paste ready with specific package versions, Docker commands, and a complete FastAPI app. All code examples are concrete Python with real imports and working logic, not pseudocode.

3 / 3

Workflow Clarity

The Quick Start has a clear 5-step sequence, but there are no validation checkpoints (e.g., verify Redis is running before starting the app, verify the API responds correctly). The Known Issues section provides good problem/solution patterns but lacks verification steps. No feedback loops for error recovery in the deployment workflow.

2 / 3

Progressive Disclosure

The 'When to Load References' section properly signals four reference files with clear descriptions, which is good structure. However, the main SKILL.md contains far too much inline content that should be in those reference files — the Known Issues section alone is ~100 lines, and Common Patterns repeats content already shown. The body doesn't practice the disclosure it promises.

2 / 3

Total

8

/

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 description that clearly defines a specific domain (production recommendation systems), lists concrete capabilities (feature stores, caching, A/B testing), and provides explicit trigger conditions covering both use cases and problem scenarios. The description is concise, uses third person voice, and includes natural keywords that users in this domain would use.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'Deploy production recommendation systems with feature stores, caching, A/B testing' and mentions specific concerns like 'personalization APIs, low latency serving, cache invalidation, experiment tracking, quality monitoring'.

3 / 3

Completeness

Clearly answers both 'what' (deploy production recommendation systems with feature stores, caching, A/B testing) and 'when' (Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues).

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'recommendation systems', 'feature stores', 'caching', 'A/B testing', 'personalization APIs', 'low latency serving', 'cache invalidation', 'experiment tracking', 'quality monitoring'. These cover a good range of terms a user working in this domain would naturally use.

3 / 3

Distinctiveness Conflict Risk

The description carves out a clear niche around production recommendation systems and their operational concerns. The combination of 'recommendation systems', 'feature stores', 'A/B testing', and 'personalization APIs' is highly specific and unlikely to conflict with other skills.

3 / 3

Total

12

/

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

10

/

11

Passed

Repository
secondsky/claude-skills
Reviewed

Table of Contents

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