vector-index-tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

1.56x

Quality

—

Does it follow best practices?

Impact

100%

1.56x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Content

65%

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

The content is highly actionable with executable code and useful reference tables, but it is a long monolithic document lacking an explicit validation-gated tuning workflow and any progressive disclosure into separate reference files.

Suggestions

Add an explicit numbered tuning workflow with validation checkpoints (e.g., 1. benchmark baseline recall/latency/memory, 2. adjust one HNSW or quantization parameter, 3. re-measure and only keep changes that meet target), so batch benchmarking has a validate-then-proceed loop.

Move the four large code templates into separate reference files under references/ and keep SKILL.md as a concise overview with one-level-deep links, improving progressive disclosure and token efficiency.

Tighten Template 2 (VectorQuantizer) to the most-used methods or reference a bundled script, reducing inline volume while preserving actionability.

Dimension	Reasoning	Score
Conciseness	The body largely avoids explaining concepts Claude already knows (it leans on tables and executable code), but ~350 lines across four full templates is more than lean; e.g. Template 2's complete VectorQuantizer class could be condensed without losing value.	2 / 3
Actionability	Real imports and executable implementations (hnswlib, qdrant_client, sklearn KMeans, numpy) with concrete parameter tables and recommendation functions make the guidance copy-paste ready.	3 / 3
Workflow Clarity	An implied tune-measure-adjust flow exists via the benchmarking template and Do's/Don'ts, but there is no explicit sequenced workflow with validation checkpoints, and batch benchmarking lacks the validate-then-proceed feedback loops the rubric expects.	2 / 3
Progressive Disclosure	Sections are clearly organized (Core Concepts, Templates, Best Practices, Resources) but no bundle files exist, so all content — including four large templates that could be split out — sits inline in a single monolithic SKILL.md rather than being progressively disclosed.	2 / 3
	Total	9 / 12 Passed

Description

100%

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 concretely names capabilities and provides an explicit Use-when trigger clause with natural domain terms. It cleanly answers both what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	"Optimize vector index performance for latency, recall, and memory" plus "tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure" lists several concrete, distinct actions matching the score-3 anchor.	3 / 3
Completeness	It states what ("Optimize vector index performance for latency, recall, and memory") and gives an explicit "Use when" trigger clause enumerating three concrete scenarios, satisfying both halves.	3 / 3
Trigger Term Quality	Natural practitioner terms appear — "HNSW parameters", "quantization", "vector search", "latency", "recall", "memory" — covering the phrasing a user would actually say when needing this skill.	3 / 3
Distinctiveness Conflict Risk	The vector-index-tuning niche with distinct triggers (HNSW, quantization, vector search scaling) is unlikely to overlap with unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (524 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: Dicklesworthstone/pi_agent_rust
Commit: e9f9b67

Reviewed: 4 days ago

Table of Contents

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