OpenSearch Launchpad

Trust Score 82/100

Guides users to design, build, and run OpenSearch-based search applications — from data ingestion and index design to semantic/hybrid search and evaluation.

triggers:opensearchsearch appvector searchbm25agentic searchindex setupdocument ingestion

GitHub SKILL.md

What it does

This skill provides a step-by-step workflow and operational scripts for building search applications with OpenSearch. It helps the agent collect sample documents, choose search strategies (BM25, dense vectors, neural sparse, hybrid, agentic), design an architecture, run OpenSearch locally with Docker, and evaluate search quality.

When to use it

Use this skill when the user asks about search application design, index setup, semantic or vector search, relevance tuning, RAG, PDF/document ingestion, or deploying search to AWS OpenSearch. It's triggered by queries about search architecture, embeddings, BM25, hybrid search, agentic search, or evaluation metrics like nDCG and precision.

What's included

Scripts: repository includes scripts to start OpenSearch and run opensearch_ops.py for operations.
References: bundled guides (dense_vector_models.md, sparse_vector_models.md, opensearch_semantic_search_guide.md, agentic_search_guide.md, document_processing_guide.md, evaluation_guide.md).
Instructions: procedural phases covering preflight checks, sample collection, preference gathering, planning, execution, evaluation, and optional AWS deployment. The skill emphasizes asking one preference question at a time and never skipping sample collection.

Compatible agents

Best used by agents with CLI and orchestration capabilities (agents that can run Docker and Python scripts) such as Claude Code, Gemini/agentic CLI flows, or Copilot-like automation agents.

Audit Summary

OpenSearch Launchpad is a well-structured skill guiding users through building search applications with OpenSearch — from data ingestion to semantic/hybrid search and evaluation. No bundled scripts to test; the skill references shared scripts in the parent repo and uses uv/uvx for execution. SKILL.md is clear, phased, and includes good error handling guidance. MCP server configuration variants are well-documented. Minor security consideration: uvx @latest auto-updates but is standard MCP practice.

Watch Out

Requires uv installed for running Python scripts
Local target requires Docker; AWS target requires AWS credentials
Scripts are shared at repo root, not bundled with this specific skill
uvx @latest runs latest version of opensearch-mcp-server-py — auto-updates on each invocation

Notes

Clean skill with no security red flags. Placeholders used for credentials in MCP config examples (not hardcoded). No destructive commands, no exfiltration patterns, no auto-update mechanisms beyond uvx @latest which is standard. Good phased workflow with clear constraints.

Information

Repository: opensearch-agent-skills
Stars: 7

Trust Score

Overall82

Security88

Code Quality78

Architecture74

Usefulness82

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