Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ArieGoldkin/ai-agent-hub --skill ai-native-developmentgit clone --depth 1 https://github.com/ArieGoldkin/ai-agent-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ariegoldkin/ai-agent-hub/ai-native-development)<a href="https://agentmods.dev/skills/ariegoldkin/ai-agent-hub/ai-native-development"><img src="https://agentmods.dev/badge/skills/ariegoldkin/ai-agent-hub/ai-native-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ariegoldkin/ai-agent-hub/ai-native-development"><img src="https://agentmods.dev/badge/skills/ariegoldkin/ai-agent-hub/ai-native-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.04114 |
| Opus 5.5 | $0.00019 | $0.01646 |
| Sonnet 5.5 | $0.00010 | $0.00823 |
| Haiku 4.5 | $0.00005 | $0.00411 |
Grade A, and why
ai-native-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 624 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Native Development
Overview
AI-Native Development focuses on building applications where AI is a first-class citizen, not an afterthought. This skill provides comprehensive patterns for integrating LLMs, implementing RAG (Retrieval-Augmented Generation), using vector databases, building agentic workflows, and optimizing AI application performance and cost.
When to use this skill:
- Building chatbots, Q&A systems, or conversational interfaces
- Implementing semantic search or recommendation engines
- Creating AI agents that can use tools and take actions
- Integrating LLMs (OpenAI, Anthropic, open-source models) into applications
- Building RAG systems for knowledge retrieval
- Optimizing AI costs and latency
- Implementing AI observability and monitoring
Why AI-Native Development Matters
Traditional software is deterministic; AI-native applications are probabilistic:
- Context is Everything: LLMs need relevant context to provide accurate answers
- RAG Over Fine-Tuning: Retrieval is cheaper and more flexible than fine-tuning
- Embeddings Enable Semantic Search: Move beyond keyword matching to understanding meaning
- Agentic Workflows: LLMs can reason, plan, and use tools autonomously
- Cost Management: Token usage directly impacts operational costs
- Observability: Debugging probabilistic systems requires new approaches
- Prompt Engineering: How you ask matters as much as what you ask
Core Concepts
1. Embeddings & Vector Search
Embeddings are vector representations of text that capture semantic meaning. Similar concepts have similar vectors.
Key Capabilities:
- Convert text to high-dimensional vectors (1536 or 3072 dimensions)
- Measure semantic similarity using cosine similarity
- Find relevant documents through vector search
- Batch process for efficiency
Detailed Implementation: See references/vector-databases.md for:
- OpenAI embeddings setup and batch processing
- Cosine similarity algorithms
- Chunking strategies (500-1000 tokens with 10-20% overlap)
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- checklists/ai-implementation-checklist.md 8.1 KB
- examples/chatbot-with-rag-example.ts 1.8 KB runs code
- references/agentic-workflows.md 14 KB
- references/function-calling.md 13 KB
- references/observability.md 13 KB
- references/rag-patterns.md 12 KB
- references/vector-databases.md 9.7 KB
- templates/agent-workflow-template.ts 11 KB runs code
- templates/rag-pipeline-template.ts 8.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 624 lines · 48 tokens per session scan A c841b70b3c29
ai-native-development is a skill published in the GitHub repository ArieGoldkin/ai-agent-hub (12 stars, last pushed 10mo ago), licensed MIT. It adds 48 tokens to every session and 4,114 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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llm-ops
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RAG Workflow Planner
Designs a complete Retrieval-Augmented Generation (RAG) pipeline for a given use case, including chunking strategy, embedding model selection, and retrieval approach.
Vector Databases
Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
ai-engineer
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rag-engineer
RAG pipeline development, vector databases, and retrieval optimization.
langchain-orchestration
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration.