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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.

77

1.60x
Quality

73%

Does it follow best practices?

Impact

74%

1.60x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.agents/skills/embeddings/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 body is lean and command-driven with genuinely executable CLI examples, avoiding the verbosity the rubric penalizes. Its weaknesses are the absence of any explicit workflow sequence or validation checkpoints (capped at 3 by the batch-operation rule), missing commands for advertised features like hyperbolic and quantization, and minor fluff (duplicated purpose statement, unsubstantiated speed multipliers).

Suggestions

Add an explicit ordered workflow (e.g., 1. init → 2. embed/batch → 3. search) with a verification step after batch operations, such as checking the stored count or running a test search before relying on the index — this directly addresses the workflow_clarity cap.

Provide concrete commands or flag examples for the features advertised in the tables (hyperbolic mode, quantization, normalization, chunking) so every advertised capability is actionable.

Trim the duplicated 'Purpose' section and the unsubstantiated speed multipliers ('75x faster', '150x-12,500x faster') to tighten conciseness.

DimensionReasoningScore

Conciseness

The body is dense — tables and copy-paste commands with no explanation of concepts Claude already knows — so it is clearly above anchor 3. Not a 5 because the 'Purpose' section repeats the frontmatter description and marketing-style speed claims ('75x faster', '150x-12,500x faster') are tokens that don't guide action. Fits anchor 4 ('efficient; minor instances that could be trimmed').

4 / 5

Actionability

All commands are concrete and executable ('npx claude-flow embeddings init --backend sqlite', 'embed --text', 'batch --file', 'search --query --top-k 5', memory store/search) and cover the common cases. Not a 5 because features advertised in the tables — hyperbolic mode, quantization, normalization, chunking — have no commands or configuration examples showing how to actually use them. Fits anchor 4 ('mostly executable guidance; concrete code or commands with minor gaps').

4 / 5

Workflow Clarity

Commands are grouped in a sensible implicit order (init → embed/batch → search) but there is no explicit sequence and no validation or verification steps anywhere. Per the guideline, batch operations (batch embed, memory store --embed) without validation cap workflow clarity at 3, which takes precedence over the simple-skill exception. Matches anchor 3 ('sequence present but checkpoints missing or implicit').

3 / 5

Progressive Disclosure

The body is well organized into clear sections (Purpose, Features, Commands, Memory Integration, Quantization, Best Practices) with all content appropriately inline for a skill of this size and no bundle files to reference — better than anchor 3's 'should be separate is inline'. Not a 5 because the file exceeds the ~50-line simple-skill threshold and minor extras (quantization table, memory integration) could be moved to reference files if the skill grows. Fits anchor 4 ('good structure; most content appropriately placed; minor organization gaps').

4 / 5

Total

15

/

20

Passed

Description

78%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 description with an explicit 'Use when'/'Skip when' trigger structure and concrete technology identifiers. Its main weaknesses are feature-listing instead of action-verb phrasing in the 'what' statement, a marketing-style speed claim ('75x faster') that adds no capability information, and slightly broad trigger terms ('knowledge retrieval') that create minor overlap risk with search/memory skills.

DimensionReasoningScore

Specificity

The description names the domain ('Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support') with concrete technologies, but it lists features rather than actions — there are no action verbs like 'search', 'store', or 'index' that anchor levels 4-5. '75x faster with agentic-flow integration' is an over-claim that adds no concrete capability, keeping it at anchor 3 ('Names domain and 1-2 concrete actions, but not comprehensive') rather than 4.

3 / 5

Completeness

It explicitly answers both: 'what' (vector embeddings with HNSW indexing, sql.js persistence, hyperbolic support) and 'when' (an explicit 'Use when:' clause with concrete trigger phrases), plus a 'Skip when' clause. Matches anchor 5 ('clearly and explicitly answers both what AND when with concrete trigger phrases'); score 4 would require the 'when' to be less explicit, which it is not.

5 / 5

Trigger Term Quality

'Use when: semantic search, pattern matching, similarity queries, knowledge retrieval' covers good natural phrases a user would say, and 'Skip when: exact text matching, simple lookups' adds negative triggers. Not a 5 because common variations like 'find similar', 'vector search', or 'embed' are missing. Not a 3 because coverage is broad and natural, not just a couple of generic keywords.

4 / 5

Distinctiveness Conflict Risk

The embeddings/semantic-search niche with specific technology names (HNSW, sql.js, Poincare hyperbolic) is mostly distinct from other skills, but 'knowledge retrieval' and 'pattern matching' are broad enough to overlap with general memory or search skills. Anchor 4 ('mostly distinct; minor overlap risk with closely related skills') fits better than 5, which requires minimal conflict risk.

4 / 5

Total

16

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
ruvnet/ruflo
Reviewed

Table of Contents

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