embeddings

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

1.60x

Quality

56%

Does it follow best practices?

Impact

74%

1.60x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

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

Quality

Content

37%

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

The skill provides a reasonable overview of embeddings capabilities with concrete CLI commands, but lacks depth in workflow sequencing and actionable integration examples. It reads more like a feature catalog than an operational guide—Claude would know what commands exist but not how to orchestrate them effectively or handle failures. The best practices section is too generic to add value.

Suggestions

Add a clear end-to-end workflow showing the init → embed → search pipeline with explicit validation steps (e.g., verify init succeeded, check embedding dimensions, validate search results).

Include expected output examples for key commands (especially search results) so Claude knows what to parse and present to users.

Replace the generic 'Best Practices' section with specific decision criteria (e.g., 'Use Int8 quantization when dataset > 10K vectors; use hyperbolic when data has tree-like hierarchy with depth > 3').

Add error handling guidance for batch operations, including how to detect and recover from partial failures.

Dimension	Reasoning	Score
Conciseness	The feature table and quantization table add some value but the 'Best Practices' section is generic advice Claude already knows. The overall structure is reasonably lean but could be tighter—the feature table largely repeats the description metadata.	2 / 3
Actionability	Provides concrete CLI commands which is good, but lacks executable code examples showing programmatic usage, expected output formats, or how to interpret results. The commands are surface-level without showing configuration options, error handling, or real integration patterns.	2 / 3
Workflow Clarity	There is no clear multi-step workflow sequencing. Commands are listed independently without showing how they connect (e.g., init → embed → search pipeline). No validation steps, no error recovery guidance, and no checkpoints for batch operations which are potentially destructive.	1 / 3
Progressive Disclosure	Content is organized into logical sections with headers, which is decent. However, there are no references to deeper documentation, no bundle files to point to, and some content (like the quantization table) could benefit from linking to more detailed configuration guides. For a skill of this complexity, more layering would help.	2 / 3
	Total	7 / 12 Passed

Description

75%

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 has strong structural completeness with explicit 'Use when' and 'Skip when' clauses, and occupies a clear niche. However, it leans heavily on technical jargon rather than describing concrete user-facing actions, and the '75x faster with agentic-flow integration' claim reads as marketing fluff rather than useful selection criteria. The actual capabilities (what the skill does) could be more concretely stated as actions rather than abstract concepts.

Suggestions

Replace abstract use cases with concrete actions, e.g., 'Indexes documents as vector embeddings, queries nearest neighbors, retrieves semantically similar content' instead of listing categories.

Remove the '75x faster with agentic-flow integration' marketing claim and replace with actionable information about what the skill actually does or when to use it.

Dimension	Reasoning	Score
Specificity	Names the domain (vector embeddings) and some technical specifics (HNSW indexing, sql.js persistence, hyperbolic support), but the actual actions/capabilities are described vaguely as use cases ('semantic search, pattern matching') rather than concrete actions the skill performs (e.g., 'index documents', 'query nearest neighbors', 'store embeddings').	2 / 3
Completeness	Clearly answers both 'what' (vector embeddings with HNSW indexing, sql.js persistence, hyperbolic support) and 'when' with explicit 'Use when' and 'Skip when' clauses that provide clear trigger guidance for skill selection.	3 / 3
Trigger Term Quality	Includes some relevant terms like 'semantic search', 'similarity queries', 'knowledge retrieval', and 'pattern matching' that users might say. However, it's heavy on technical jargon ('HNSW indexing', 'hyperbolic support', 'sql.js persistence') that users are unlikely to use in natural requests, and misses common variations like 'find similar', 'vector search', 'embeddings', 'nearest neighbor'.	2 / 3
Distinctiveness Conflict Risk	Highly distinctive with a clear niche around vector embeddings and semantic search. The specific technical stack (HNSW, sql.js) and the 'Skip when' clause for exact text matching help clearly differentiate this from text search or database query skills.	3 / 3
	Total	10 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/ruflo
Commit: 28c81c0

Reviewed: about 22 hours ago

Table of Contents

Discovery Implementation Validation

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