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save-pattern

Store architectural and procedural patterns in AgentDB's patterns table with semantic embeddings.

Updated Jul 2, 2025
Search Tags:#embeddings#patterns#agentdb#knowledge-management#store-patterns#pattern-search
One-click install
npx skills add https://github.com/dug-21/neural-data-platform --skill save-pattern
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: save-pattern
Source: https://github.com/dug-21/neural-data-platform/tree/main/.claude/skills/save-pattern
Command: npx skills add https://github.com/dug-21/neural-data-platform --skill save-pattern

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a centralized way to store reusable application patterns in AgentDB's patterns table, enabling consistent knowledge sharing across agents through semantic embeddings.

Core Features & Use Cases

  • Store architectural and procedural patterns with semantic embeddings for efficient search via agentdb_pattern_search.
  • Prevent duplicates by first searching existing patterns before storage, ensuring high-quality pattern maintenance.
  • Retrieve pattern usage statistics with agentdb_pattern_stats to monitor adoption and quality.

Quick Start

Use the save-pattern skill to store a new pattern by calling mcp__agentdb__agentdb_pattern_store with taskType, approach, and optional tags and successRate. Example: mcp__agentdb__agentdb_pattern_store( taskType="architecture:domain-adapter", approach="Domain Adapter Pattern for Data Sources: All data sources implement the Source trait, fetch() -> Vec<TimeSeriesPoint>, and health_check() -> HealthStatus. Related files: core/src/traits.rs, core/src/sources/http_poll.rs", successRate=0.95, tags=["architecture","pattern","agentdb"] )

Frequently Asked Questions about save-pattern

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I store reusable application patterns in AgentDB?▼

Store reusable application patterns in AgentDB by calling the pattern store function with a task type, approach description, and optional tags or success rate to save architectural knowledge with semantic embeddings for retrieval.

What is the best way to search existing patterns before storing duplicates in AgentDB?▼

Search existing patterns before storing duplicates by using the pattern search function to query the AgentDB patterns table, ensuring high-quality maintenance and preventing redundant entries with overlapping semantic embeddings.

Can I track pattern usage statistics and quality metrics in AgentDB?▼

Track pattern usage statistics in AgentDB by retrieving adoption and quality metrics with the pattern stats function, which monitors how often stored patterns are accessed and their overall success rates.

Do I need semantic embeddings to retrieve architectural patterns from AgentDB?▼

Semantic embeddings are required to efficiently retrieve architectural and procedural patterns from AgentDB, as they enable the pattern search function to match query intent against stored approaches.

What parameters are required to save a pattern in AgentDB?▼

Saving a pattern in AgentDB requires the taskType and approach parameters to define the pattern category and implementation details, with optional successRate and tags for tracking usage quality.

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