Source profileQuality 69/100

wshobson/agents/plugins/llm-application-dev/skills/similarity-search-patterns/SKILL.md

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Source repository stars
38,313
Declared platforms
0
Static risk flags
1
Last source update
2026-07-22
Source checked
2026-07-28
DecisionCompatibilityInstallWorkflowPermissionsEvidenceSource

Decision brief

What it does—and where it fits

Patterns for implementing efficient similarity search in production systems.

Best for

  • Building semantic search systems
  • Implementing RAG retrieval
  • Creating recommendation engines

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/wshobson/agents --skill "plugins/llm-application-dev/skills/similarity-search-patterns"
Safe inspection promptEditorial

Inspect the Agent Skill "similarity-search-patterns" from https://github.com/wshobson/agents/blob/c4b82b0ad771190355eb8e204b1329732a18449a/plugins/llm-application-dev/skills/similarity-search-patterns/SKILL.md at commit c4b82b0ad771190355eb8e204b1329732a18449a. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    When to Use This Skill

    Building semantic search systems

    Building semantic search systemsImplementing RAG retrievalCreating recommendation engines
  2. 02

    Core Concepts

    Review the “Core Concepts” section in the pinned source before continuing.

    Review and apply the “Core Concepts” source section.
  3. 03

    1. Distance Metrics

    Review the “1. Distance Metrics” section in the pinned source before continuing.

    Review and apply the “1. Distance Metrics” source section.
  4. 04

    2. Index Types

    Review the “2. Index Types” section in the pinned source before continuing.

    Review and apply the “2. Index Types” source section.

Permission review

Static risk signals and limitations

Reads files

low · line 42

The documentation asks the agent to read local files, directories, or repositories.

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

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score69/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars38,313SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
wshobson/agents
Skill path
plugins/llm-application-dev/skills/similarity-search-patterns/SKILL.md
Commit
c4b82b0ad771190355eb8e204b1329732a18449a
License
MIT
Collected
2026-07-28
Default branch
main
View the original SKILL.md

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