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azure-search-documents-ts

Build search applications with vector, hybrid, and semantic search capabilities.

52

Quality

57%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/azure-search-documents-ts/SKILL.md

The canonical home for this skill is azure-search-documents-ts in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

65%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.

The content is a lean, largely executable Azure AI Search TypeScript reference covering the major search modalities with minimal padding. Its weaknesses are the absence of validation/feedback loops for batch operations and no progressive disclosure — everything is inlined in one file rather than split into referenced materials.

Suggestions

Add explicit validation checkpoints and error-handling feedback loops around batch and index-management operations (e.g. verify index creation, check `result.results` for failed actions, retry on failure) to lift workflow clarity above the batch-operation cap.

Split the bulk API/type reference and per-search-mode examples into bundled reference files (e.g. references/search-modes.md, references/types.md) and point to them from the SKILL.md overview to improve progressive disclosure.

Resolve the stubbed `getEmbedding` and `[...]` placeholders with concrete guidance or a clearly signaled example so the code is fully copy-paste executable.

DimensionReasoningScore

Conciseness

The body is almost entirely terse, executable TypeScript with section headers and no padding of concepts Claude already knows; only the informational 'Key Types' import block and the redefined semantic index are minor trim opportunities, placing it just below the lean score-5 anchor.

4 / 5

Actionability

It provides concrete, copy-paste-ready code across install, auth, indexing, and all search modes covering common cases, but stubbed embedding functions ('Your embedding function') and `[...]`/placeholder arrays are minor gaps keeping it from fully executable score-5.

4 / 5

Workflow Clarity

Operations are presented as discrete sections but lack an explicit sequence and any validation checkpoints; because the skill includes batch document operations and index management, the missing-validation cap holds workflow clarity at 3.

3 / 5

Progressive Disclosure

The body is well-organized with clear headers, but a large API/type reference with multiple search-mode examples is inlined entirely in SKILL.md with no bundle files or references, matching 'some structure but content that should be separate is inline'.

3 / 5

Total

14

/

20

Passed

Description

50%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.

The description gives a clear, concise 'what' focused on search modalities but entirely lacks a 'when to use' trigger clause and product-specific naming. It is competent but capped at the midpoint across all dimensions by the missing-trigger and partial-keyword guidance.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when building Azure AI Search solutions or when the user mentions Azure Search, vector search, hybrid search, or semantic ranking'.

Include the product name 'Azure AI Search' in the description to improve distinctiveness and reduce conflict with generic search skills.

Broaden keyword coverage with synonyms and file/extension cues users actually say (e.g. 'Azure AI Search', 'Cognitive Search', 'semantic ranking', 'vector embeddings').

DimensionReasoningScore

Specificity

The description names the search domain and lists concrete capability types ('vector, hybrid, and semantic search'), but uses a single action verb with three variants rather than multiple distinct actions, matching the 'names domain and 1-2 concrete actions' anchor.

3 / 5

Completeness

There is a clear 'what' (build search applications with vector/hybrid/semantic capabilities) but no 'Use when...' or equivalent trigger clause, so per the missing-trigger cap completeness cannot exceed 3.

3 / 5

Trigger Term Quality

It includes relevant keywords ('search applications', 'vector', 'hybrid', 'semantic search') but misses common natural variations users would say, such as 'Azure AI Search' or 'Cognitive Search', so coverage is partial rather than good.

3 / 5

Distinctiveness Conflict Risk

The vector/hybrid/semantic qualifiers narrow it, but the description omits the product name 'Azure AI Search', leaving overlap risk with other search skills and placing it at 'somewhat specific but could still overlap'.

3 / 5

Total

12

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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