📦
content-creator
88Create Brand-Consistent Marketing Content
Marketing teams need content that stays consistent across channels. This skill helps plan, write, analyze, and optimize content for brand voice and SEO.
Writing & Content View
Skills embedding-strategies
📦
embedding-strategies
Content revision r2 Safe ⚙️ External commands🌐 Network access
Design Better Embedding Pipelines
Choosing an embedding model and chunking strategy without evidence can reduce retrieval quality and increase cost. This skill provides comparison criteria, implementation patterns, and evaluation metrics for vector search.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "embedding-strategies" from https://skillstore.io/skills/sickn33-embedding-strategies.md and its manifest at https://skillstore.io/api/skills/sickn33-embedding-strategies/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "embedding-strategies". Choose an embedding model for multilingual support articles with moderate traffic and strict cost limits.
Expected outcome:
- Primary candidate: multilingual-e5-large for local multilingual retrieval.
- Hosted alternative: text-embedding-3-small when operational simplicity is more important than local control.
- Validate both choices on labeled support queries before deployment.
Using "embedding-strategies". Recommend chunking for long Markdown product manuals.
Expected outcome:
Split by heading hierarchy, then recursively split oversized sections. Preserve titles as metadata and test 10 to 20 percent overlap.
Using "embedding-strategies". Define a model migration evaluation for an existing semantic search index.
Expected outcome:
- Freeze a representative query and relevance set.
- Compare recall at 10, MRR, NDCG at 10, latency, storage, and cost.
- Re-embed the full index because vectors from different models are not compatible.
Security Audit
Safev5 • 7/23/2026 Open versioned report
All 17 static findings are false positives caused by Markdown formatting, documentation links, ordinary Python dictionary access, or prose. No prompt injection, malicious command execution, system reconnaissance, or semantic security issue was found.
1
Files scanned
500
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚙️ External commands (12)
🌐 Network access (3)
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/sickn33-embedding-strategies/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/sickn33-embedding-strategies?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/sickn33-embedding-strategies?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/sickn33-embedding-strategies/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/sickn33-embedding-strategies.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). embedding-strategies security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-embedding-strategies/audits/5BibTeX citation
@techreport{sickn33-sickn33-embedding-strategies-2026,
author = {sickn33},
title = {embedding-strategies security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-embedding-strategies/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "embedding-strategies security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-embedding-strategies/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-embedding-strategies:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
wshobson Recommended
wshobson-embedding-strategies
Skillstore Score 79
Evidence Confidence High
Skillstore usage 12
Updated
2026-09-09
sickn33 Current
sickn33-embedding-strategies
Skillstore Score 78
Evidence Confidence High
Skillstore usage 11
Updated
2026-09-09
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Plan a RAG Index
Select a model and chunking method for a document collection with clear cost, language, and latency constraints.
Compare Embedding Models
Create an evaluation plan using labeled queries and retrieval metrics before changing production models.
Improve Semantic Search
Diagnose weak retrieval by reviewing preprocessing, chunk overlap, normalization, and model suitability.
Try These Prompts
Choose an Embedding Model
Recommend an embedding model for [content type] in [languages]. Compare quality, latency, hosting, dimensions, token limits, and estimated cost.
Design a Chunking Strategy
Design a chunking strategy for [document format] with [average length]. Specify chunk size, overlap, semantic boundaries, metadata, and validation checks.
Build a Retrieval Evaluation
Create an offline evaluation plan for [search task]. Define a labeled dataset, baseline, precision, recall, MRR, NDCG, and acceptance thresholds.
Plan a Domain Embedding Pipeline
Design a production embedding pipeline for [domain]. Include preprocessing, batching, model choice, dimension strategy, caching, versioning, monitoring, privacy, and migration testing.
Best Practices
- Evaluate models on representative queries and labeled relevant documents before production use.
- Preserve semantic boundaries and useful metadata when chunking documents.
- Version models, preprocessing, dimensions, and indexes together for reproducible migrations.
Avoid
- Do not choose a model only from public leaderboard scores.
- Do not mix vectors from different models or dimensions in one index.
- Do not send sensitive documents to hosted APIs without approved privacy controls.
Frequently Asked Questions
Does this skill run embedding models?
No. It provides guidance and example patterns that require implementation, dependencies, credentials, and testing.
Can it recommend hosted and local models?
Yes. It compares hosted OpenAI and Voyage options with local Sentence Transformers models.
Does it cover document chunking?
Yes. It covers token, sentence, semantic section, and recursive character strategies.
Which retrieval metrics are included?
The skill covers precision at k, recall at k, MRR, NDCG at k, and vector similarity measures.
Does it support multilingual search planning?
Yes. It identifies a multilingual model and helps compare language coverage with cost and operational constraints.
Is the example code production ready?
No. Validate dependencies, correctness, error handling, privacy controls, rate limits, and performance in the target environment.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/embedding-strategiesRef
88a8e9a07f4c54ab105c1c41b6267c287146b07b
Maintenance freshness
7/26/2026
Usage
7 downloads · 106 views
File structure
📄 SKILL.md
More from sickn33
View all📦
prompt-engineering-patterns
85Improve LLM Prompts With Proven Patterns
Inconsistent prompts waste time and make AI outputs hard to trust. This skill guides prompt design with reusable patterns, examples, evaluation steps, and optimization workflows.
Productivity View
📦
senior-architect
81Review Architecture Workflows and Project Paths
Architecture reviews need a consistent starting point and clear project boundaries. This skill provides workspace-scoped tool templates, command examples, and generic design references.
Coding & Development View
📦
senior-fullstack
81Review Fullstack Project Foundations
Fullstack project setup can require many tools and checks. This skill provides basic local wrappers, common commands, and general engineering guidance.
Coding & Development View
📦
prompt-engineer
81Optimize AI Prompts With Prompt Engineer
Writing clear prompts is hard when goals are vague or complex. This skill turns rough requests into structured prompts using proven prompt frameworks.
Productivity View
📦
product-manager-toolkit
80Prioritize Product Work With Research Insights
Product teams need faster ways to rank features, synthesize interviews, and document decisions. This skill provides RICE scoring, interview analysis, and PRD templates for structured planning.
Productivity View
Related Skills
View all📦
agentdb-semantic-vector-search
80Build AgentDB Semantic Search
by DNYoussef
Teams need relevant document retrieval for RAG and knowledge bases. This skill guides AgentDB setup, embedding storage, indexing, API search, and relevance tuning.
Data & Analytics View
📦
agentdb-vector-search
78Build AgentDB Vector Search
by DNYoussef
Teams need fast semantic retrieval for documents, RAG systems, and knowledge bases. This skill gives AgentDB setup, search patterns, and tuning guidance.
Data & Analytics View
📦
surrealdb-python
72Build SurrealDB Python Applications
by ActiveInferenceInstitute
SurrealDB projects often need one guide for documents, graph traversal, vectors, and live queries. This skill gives Python-focused patterns for designing, querying, and tuning multi-model database features.
Data & Analytics View
📦
senior-ml-engineer
81Deploy Production ML Systems
by davila7
Production ML systems are hard to design, deploy, monitor, and improve reliably. This skill gives Claude, Codex, and Claude Code structured ML engineering guidance.
Data & Analytics View
📦
agentdb-vector-search-optimization
80Optimize AgentDB Vector Search
by DNYoussef
Large vector databases can become slow, memory-heavy, and hard to benchmark. This skill provides a structured AgentDB optimization workflow for quantization, HNSW indexing, caching, and validation.
Data & Analytics View
📦
advanced-agentdb-vector-search-implementation
79Build Advanced AgentDB Vector Search
by DNYoussef
Distributed vector search needs careful replication, metrics, monitoring, and deployment planning. This skill gives a phased AgentDB workflow for scalable hybrid search systems.
Data & Analytics View