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Embedding Strategies · dicklesworthstone | DecimalAI

ai mlby dicklesworthstone

Embedding Strategies

dicklesworthstone/embedding-strategies

Source dicklesworthstone/pi_agent_rust ↗License NOASSERTION

Select and optimize embedding models for semantic search and RAG applications.

SkillScore86Maintained

Automatic

AI Engineer pack

Last verified 8/29/2026

What it changes

Passed 18 of 21 test cases with the skill, and 14 of 21 without it.

Without skill68%

With skill86%

Checks it passed that the model alone missed

Recommends model voyage-code-3
Recommends model voyage-finance-2
Selects voyage-3-large as the primary embedding model

and 2 more in the full report

4 fixed15 passed either way3 still failing0 broke

Pass-rate lift: +18 pp
Tokens: +227%
Agent turns: 0%
Response time: -2%

Model gemini-3.6-flashMeasured 8/29/2026v119 of 22 cases passed with skill · 21 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/dicklesworthstone-embedding-strategies/SKILL.md https://app.decimal.ai/s/dicklesworthstone-embedding-strategies/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add dicklesworthstone-embedding-strategies, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/dicklesworthstone-embedding-strategies/ and pins it in .decimal/skills.lock

decimalai skills add dicklesworthstone-embedding-strategies

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/dicklesworthstone-embedding-strategies/SKILL.md https://app.decimal.ai/s/dicklesworthstone-embedding-strategies/SKILL.md

Raw file: /s/dicklesworthstone-embedding-strategies/SKILL.md ↗ · machine-readable summary at /s/dicklesworthstone-embedding-strategies.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 3

Prompt

We are building a retrieval-augmented generation pipeline using Anthropic Claude 3.5 Sonnet. We currently default to OpenAI text-embedding-3-large. Is there a different embedding provider and model line specifically recommended for Anthropic Claude integration, and what model should we select?

What was checked, without and with the skill
Checked	Without skill	With skill
Recommends Voyage AI as the embedding provider	Passed	Passed
Selects voyage-3-large as the primary embedding model	Missed	Passed

case-02 · gemini-3.6-flash · 8/29/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md603 linessingle file

Embedding Strategies

Guide to selecting and optimizing embedding models for vector search applications.

When to Use This Skill

Choosing embedding models for RAG
Optimizing chunking strategies
Fine-tuning embeddings for domains
Comparing embedding model performance
Reducing embedding dimensions
Handling multilingual content

Core Concepts

1. Embedding Model Comparison (2026)

Model	Dimensions	Max Tokens	Best For
voyage-3-large	1024	32000	Claude apps (Anthropic recommended)
voyage-3	1024	32000	Claude apps, cost-effective
voyage-code-3	1024	32000	Code search
voyage-finance-2	1024	32000	Financial documents
voyage-law-2	1024	32000	Legal documents
text-embedding-3-large	3072	8191	OpenAI apps, high accuracy
text-embedding-3-small	1536	8191	OpenAI apps, cost-effective
bge-large-en-v1.5	1024	512	Open source, local deployment
all-MiniLM-L6-v2	384	256	Fast, lightweight
multilingual-e5-large	1024	512	Multi-language

2. Embedding Pipeline

Document → Chunking → Preprocessing → Embedding Model → Vector
                ↓
        [Overlap, Size]  [Clean, Normalize]  [API/Local]

Templates

Track record

Benchmark

18 / 21

test cases passed, vs 14 / 21 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dicklesworthstone / pi_agent_rust

tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/embedding-strategies/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/10/2026Version v1

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Embedding Strategies

When to Use This Skill

Core Concepts

1. Embedding Model Comparison (2026)

2. Embedding Pipeline

Templates

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills