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Agent skill

Similarity Search Patterns

by wshobson in wshobson/agents

Covers distance metrics, index types and tuning for similarity search on vector databases, from semantic search to RAG retrieval.

MITAuto-check passedDatabases

Install Similarity Search Patterns

skills CLI
$ npx skills add wshobson/agents --skill similarity-search-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wshobson/agents similarity-search-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/llm-application-dev/skills/similarity-search-patterns .claude/skills/similarity-search-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
similarity-search-patterns
GitHub stars
40k
Used in
10 other repos
Token cost
~577 tokens
SKILL.md length
159 words
Files
2 (incl. references)
Skills in repo
142
Repo updated
First seen
Licence
MIT

At a glance

Covers distance metrics, index types and tuning for similarity search on vector databases, from semantic search to RAG retrieval.

  • Works in 2 steps: Distance Metrics → Index Types
  • Building a semantic search system on a vector database
  • SKILL.md covers When to Use This Skill, Core Concepts, Templates and detailed worked… and Best Practices
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Patterns for building similarity search in production systems cover semantic search, RAG retrieval, recommendation engines, latency tuning, scaling to millions of vectors and combining semantic with keyword search. The core concepts include distance metrics, with cosine for normalized embeddings, Euclidean for raw embeddings, dot product when magnitude matters and Manhattan for sparse vectors, plus an overview of index types.

Guidance favors HNSW for most cases, tuning ef_search and nprobe to trade recall for speed, combining semantic and keyword search, measuring recall and pre-filtering to shrink the search space. It warns against skipping evaluation, over-indexing early, ignoring tail latency and forgetting that vector storage costs add up. Full templates and worked examples sit in references/details.md.

When your agent uses it

  • Building a semantic search system on a vector database
  • Choosing a distance metric and index type for embeddings
  • Reducing retrieval latency when scaling to millions of vectors
  • Combining semantic and keyword search

Example prompts

  • “Design similarity search for our product catalog embeddings and pick an index type.”
  • “Our vector search is slow at scale. Tune the index for better latency without losing recall.”
  • “Add hybrid keyword and semantic search to our RAG retrieval.”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Distance Metrics
  2. Index Types

What it can do on your machine

Read from SKILL.md and the folder at commit 46891e7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Similarity Search Patterns loads about 577 tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 159 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~577
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 159 words, ~577 tokens.

Download SKILL.mdSave it as .claude/skills/similarity-search-patterns/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
similarity-search-patterns
description
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Similarity Search Patterns

Patterns for implementing efficient similarity search in production systems.

When to Use This Skill

  • Building semantic search systems
  • Implementing RAG retrieval
  • Creating recommendation engines
  • Optimizing search latency
  • Scaling to millions of vectors
  • Combining semantic and keyword search

Core Concepts

1. Distance Metrics

| Metric | Formula | Best For | | ------------------ | ------------------ | --------------------- | --- | -------------- | | Cosine | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings | | Euclidean (L2) | √Σ(a-b)² | Raw embeddings | | Dot Product | A·B | Magnitude matters | | Manhattan (L1) | Σ | a-b | | Sparse vectors |

2. Index Types
┌─────────────────────────────────────────────────┐
│                 Index Types                      │
├─────────────┬───────────────┬───────────────────┤
│    Flat     │     HNSW      │    IVF+PQ         │
│ (Exact)     │ (Graph-based) │ (Quantized)       │
├─────────────┼───────────────┼───────────────────┤
│ O(n) search │ O(log n)      │ O(√n)             │
│ 100% recall │ ~95-99%       │ ~90-95%           │
│ Small data  │ Medium-Large  │ Very Large        │
└─────────────┴───────────────┴───────────────────┘

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's
  • Use appropriate index - HNSW for most cases
  • Tune parameters - ef_search, nprobe for recall/speed
  • Implement hybrid search - Combine with keyword search
  • Monitor recall - Measure search quality
  • Pre-filter when possible - Reduce search space
Don'ts
  • Don't skip evaluation - Measure before optimizing
  • Don't over-index - Start with flat, scale up
  • Don't ignore latency - P99 matters for UX
  • Don't forget costs - Vector storage adds up

© wshobson, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/llm-application-dev/skills/similarity-search-patterns of wshobson/agents.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit 46891e7

Used in 10 other repositories

We found 20 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in wshobson/agents, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Similarity Search Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Similarity Search Patterns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Similarity Search Patterns this skillwshobson/agents40k10 repos~577Automated safety check: PassMIT
Qdrant Search Qualitygithub/awesome-copilot40k1 repos~336Automated safety check: PassMIT
Postgrestimescale/pg-aiguide1.9k—~941Automated safety check: PassApache-2.0
DBoracle/skills877—~1.4kAutomated safety check: PassUPL-1.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
FAISS Similarity SearchOrchestra-Research/AI-Research-SKILLs13k6 repos~1.3kAutomated safety check: PassMIT

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Categories

Questions about Similarity Search Patterns

What does Similarity Search Patterns do?

Covers distance metrics, index types and tuning for similarity search on vector databases, from semantic search to RAG retrieval. Patterns for building similarity search in production systems cover semantic search, RAG retrieval, recommendation engines, latency tuning, scaling to millions of vectors and combining semantic with keyword search. The core concepts include distance metrics, with cosine for normalized embeddings, Euclidean for raw embeddings, dot product when magnitude matters and Manhattan for sparse vectors, plus an overview of index types.

When should I use Similarity Search Patterns?

Similarity Search Patterns fits situations like: building a semantic search system on a vector database; choosing a distance metric and index type for embeddings; reducing retrieval latency when scaling to millions of vectors; combining semantic and keyword search.

How do I install Similarity Search Patterns in Claude Code?

Run `npx skills add wshobson/agents --skill similarity-search-patterns -a claude-code`. Or copy the skill folder (plugins/llm-application-dev/skills/similarity-search-patterns in wshobson/agents) into .claude/skills/similarity-search-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Similarity Search Patterns in Codex?

Run `npx skills add wshobson/agents --skill similarity-search-patterns -a codex`. Or copy the skill folder (plugins/llm-application-dev/skills/similarity-search-patterns in wshobson/agents) into .agents/skills/similarity-search-patterns in your project. Codex loads it when a task matches its description.

Can I use Similarity Search Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wshobson/agents --skill similarity-search-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/similarity-search-patterns, .gemini/skills/similarity-search-patterns, .github/skills/similarity-search-patterns and .opencode/skills/similarity-search-patterns in your project.

What does Similarity Search Patterns need to run?

SKILL.md names no scripts, command-line tools or credentials: Similarity Search Patterns is instructions for the agent only.

Does Similarity Search Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Similarity Search Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Similarity Search Patterns use?

Similarity Search Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Similarity Search Patterns use?

About 577 tokens (SKILL.md is roughly 2.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.9k tokens, read only when the agent opens those files.

What are the alternatives to Similarity Search Patterns?

Skills that share tags, products or a category with Similarity Search Patterns: Qdrant Search Quality (github/awesome-copilot, 40k stars), Postgres (timescale/pg-aiguide, 1.9k stars), DB (oracle/skills, 877 stars) and Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Similarity Search Patterns?

wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,314 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.

Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.