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redis-core

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.

70

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

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

A well-organized reference skill that stays lean, gives concrete commands and key patterns, and correctly offloads detail to two real one-level-deep reference files. The main gap is the absence of any inline executable example in the body itself.

Suggestions

Add one short inline code snippet (e.g., a Hash field update with redis.hset) in the body so the core pattern is copy-paste ready without opening a reference.

Trim the opening paragraph ('Foundational guidance for modeling data in Redis. Covers ...') since the section headers already convey the same information.

For the data-structure table, consider a one-line 'avoid when' note per row to make the decision guidance more bidirectional.

DimensionReasoningScore

Conciseness

The body is lean — a decision table, key patterns, and rules of thumb — and assumes Claude knows Redis basics, though the opening orientation paragraph is mild padding that could be trimmed.

4 / 5

Actionability

Concrete command names (INCR/DECR, SADD/SISMEMBER, ZADD/ZRANGE) and explicit key patterns make the guidance executable; inline code is deferred to references, which is appropriate for this guidance skill but leaves minor gaps in the body itself.

4 / 5

Workflow Clarity

Two clearly delineated decision areas (data structure by access pattern, key naming) with a 'When to apply' section; no destructive or batch operations requiring validation, and the decision process is unambiguous, though it is not a single crisp multi-step workflow.

4 / 5

Progressive Disclosure

A concise overview body points via clearly signaled markdown links to two real, one-level-deep reference files (choose-data-structure.md, key-naming.md) plus an external links section, making navigation easy.

5 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, well-scoped description that names concrete actions, enumerates the supported data types, and provides explicit 'Use when' trigger guidance. It uses imperative third-person voice throughout with no over-claims.

DimensionReasoningScore

Specificity

Enumerates concrete actions ('choose the right data structure', 'use consistent colon-separated key names') and lists all eight data types, giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Clearly states what it does and follows with an explicit 'Use when ...' clause listing concrete design scenarios, answering both what and when.

5 / 5

Trigger Term Quality

Natural trigger phrases are present ('caching objects', 'counters', 'leaderboards', 'membership sets', 'session stores', 'reviewing/cleaning up Redis key naming'), but a few common synonyms a user might say are missing.

4 / 5

Distinctiveness Conflict Risk

Scoped tightly to Redis data modeling and key naming with niche-specific triggers, making conflict with other skills minimal.

5 / 5

Total

19

/

20

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
redis/agent-skills
Reviewed

Table of Contents

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