Similarity Search Patterns
Guides the agent through designing semantic and vector similarity search systems
- Repository
- wshobson/agents
- Stars
- 42,095
- Licence
- MIT
- Files
- 2 files
What it does
When installed, this skill gives the agent a set of reference patterns for building search that finds results by meaning rather than exact keywords, using vector embeddings. The agent can then explain distance metrics such as cosine and Euclidean, compare index types like Flat, HNSW and IVF+PQ, and suggest practices such as hybrid search, tuning recall versus speed, and watching storage costs. It also points to a reference file with concrete code templates and worked examples that the agent reads when needed.
Great for
- Software engineers building semantic search or retrieval for RAG applications
- Teams deciding which vector index or distance metric suits their data size and latency needs
- Developers who want a checklist of performance and evaluation practices before scaling a vector search system
Skip it if
- Simple keyword search on a small website, where a standard database search is enough and vector infrastructure adds cost and complexity
- Non-technical users looking for a finished search product, since the skill gives guidance and templates rather than a ready-made tool
- Production decisions taken without testing, because the skill's performance figures are general approximations and recall must be measured on your own data
Try asking
- “Design a semantic search pipeline for our support articles using embeddings”
- “Should I use HNSW or IVF+PQ for 5 million product vectors with a 50ms latency budget?”
- “Add hybrid keyword and vector search to our document retrieval system”
- “Show me a template for nearest neighbor queries with pre-filtering by category”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 2 original files, the MIT licence and an attribution note.
similarity-search-patterns.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip similarity-search-patterns.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip similarity-search-patterns.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Ask for a specific index or metric recommendation by giving your data size, embedding model and latency target, so the agent can apply the guidance to your case.
- Have the agent write an evaluation harness early so you can measure recall on your own data rather than relying on the approximate figures in the skill.
- Review any code templates the agent adapts for your vector database, since the skill does not name a specific library and the examples must be checked against your versions.
What's inside
- references/details.md
- SKILL.md
Redistributed unmodified from wshobson/agents under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.