embeddings

Vector embeddings configuration and semantic search

Quality

40%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./src/skills/bundled/embeddings/SKILL.md

Quality

Content

57%

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 TypeScript code and clear chat commands for embeddings configuration and semantic search. However, it suffers from being a monolithic reference document with no progressive disclosure or external file references, and lacks explicit workflow sequencing and validation steps. The best practices section adds little value for Claude.

Suggestions

Add a concise Quick Start section at the top and move the detailed API reference, provider tables, and use cases into separate referenced files (e.g., API_REFERENCE.md, PROVIDERS.md)

Add explicit error handling examples and validation steps (e.g., verify embedding dimensions match expected, handle API failures, confirm storage succeeded)

Remove the 'Best Practices' section — these are generic tips Claude already knows and don't add actionable value

Dimension	Reasoning	Score
Conciseness	The content is reasonably structured but includes some unnecessary sections like the 'Best Practices' tips that are generic advice Claude already knows (use caching, batch requests, monitor costs). The provider comparison table and use cases section add bulk that could be trimmed or referenced externally.	2 / 3
Actionability	The skill provides fully executable TypeScript code examples with concrete API calls, configuration objects, and specific provider/model names. Chat commands are clearly listed with exact syntax. Code is copy-paste ready.	3 / 3
Workflow Clarity	The content is primarily an API reference rather than a multi-step workflow, but the implicit workflow (configure → embed → store → search) lacks explicit sequencing or validation checkpoints. There's no guidance on error handling, verifying embeddings were stored correctly, or what to do when API calls fail.	2 / 3
Progressive Disclosure	This is a monolithic wall of content (~180 lines) with no references to external files. The provider details, model tables, use cases, and full API reference are all inline when they could be split into separate reference files. There's no overview/quick-start section that points to deeper content.	1 / 3
	Total	8 / 12 Passed

Description

22%

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 description is too terse and lacks both concrete actions and explicit trigger guidance. While it names a recognizable domain (vector embeddings and semantic search), it fails to explain what specific tasks the skill performs or when Claude should select it. It reads more like a topic label than a functional skill description.

Suggestions

Add specific concrete actions, e.g., 'Configures vector embedding models, creates embedding indexes, performs similarity queries, and tunes search relevance parameters.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about vector databases, embedding configuration, similarity search, cosine similarity, RAG pipelines, or semantic retrieval.'

Include common user-facing variations and file/tool references (e.g., 'Pinecone', 'Weaviate', 'FAISS', '.embeddings', 'vector store') to improve trigger term coverage and distinctiveness.

Dimension	Reasoning	Score
Specificity	The description names a domain ('vector embeddings', 'semantic search') but does not list any concrete actions. There are no verbs describing what the skill actually does—no 'configure', 'generate', 'query', etc.	1 / 3
Completeness	The description weakly addresses 'what' (configuration and semantic search) but provides no 'when' clause or explicit trigger guidance. The lack of a 'Use when...' clause caps this at 2 per the rubric, and the 'what' is also very weak, so it scores 1.	1 / 3
Trigger Term Quality	It includes some relevant keywords like 'vector embeddings', 'semantic search', and 'configuration', which users might mention. However, it misses common variations such as 'similarity search', 'embedding model', 'vector database', 'cosine similarity', or 'RAG'.	2 / 3
Distinctiveness Conflict Risk	The terms 'vector embeddings' and 'semantic search' are somewhat specific to a niche, but without concrete actions or explicit triggers, it could overlap with general search skills, database configuration skills, or ML/AI skills.	2 / 3
	Total	6 / 12 Passed

Validation

90%

Warnings & errors only

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

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: alsk1992/CloddsBot
Commit: e71a5f6

Reviewed: 2 months ago

Table of Contents

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