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 roedyrustam/vibes-plug --skill ai-llm-integration-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/ai-llm-integration-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/ai-llm-integration-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-llm-integration-expert/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/roedyrustam/vibes-plug/ai-llm-integration-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-llm-integration-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00078 | $0.06656 |
| Opus 5.5 | $0.00031 | $0.02662 |
| Sonnet 5.5 | $0.00016 | $0.01331 |
| Haiku 4.5 | $0.00008 | $0.00666 |
Grade A, and why
ai-llm-integration-expert 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 4d 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 — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI & LLM Integration Expert (2026 Edition)
English
Orchestration & Integration
- Frontier Models & Providers:
frontier-ai-models-expert,gemini-agent-booster,vercel-ai-sdk-expert,pydantic-ai-expert - MCP Ecosystem:
mcp-server-architect,mcp-sampling-critic,doku-mcp-server - Multi-Agent & Tooling:
multi-agent-orchestration,parallel-tool-orchestrator,speculative-multi-draft-synthesizer - Memory & Retrieval:
agentic-memory-architect,vector-db-rag-expert,graph-rag-knowledge-expert,local-slm-edge-ai-expert - Optimization & FinOps:
kv-cache-prefix-optimizer,thinking-budget-orchestrator,llm-finops-router,context-window-engineer - Safety, Governance & Quality:
ai-safety-governance-expert,prompt-injection-firewall,ai-prompt-engineering-expert,anti-slop - Lifecycle & Governance:
zero-to-prod-orchestrator,session-memory-manager
Description
Production-grade guidelines for integrating AI, Model Context Protocol (MCP), hybrid reasoning models, and Large Language Models (LLMs) into modern software architectures. Covers hybrid reasoning token streaming, Streamable HTTP MCP transports, agentic memory architectures, native context caching, RAG pipelines, and multi-model orchestration.
Trigger Conditions
- Integrating frontier reasoning models: Google Gemini 4 Pro / Gemini 4 Flash / Project Astra, Anthropic Claude 5.1 (Fable/Mythos) / Claude 5 Sonnet, OpenAI GPT-5.6 / GPT-5 / o3 / o3-mini / GPT-4.5, DeepSeek-R1 / DeepSeek-Pro / V3, or open-source weights (Llama 4, Qwen 2.5/3 Coder).
- Implementing Model Context Protocol (MCP) server or client integrations with Streamable HTTP transport or MCP Sampling.
- Building AI chatbots, copilots, or autonomous AI agent workflows (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai, Vercel AI SDK 5.x/6.x).
- Managing streaming reasoning tokens (
<think>chunks) separately from final output in user interfaces. - Implementing hybrid RAG with vector databases (Supabase pgvector HNSW, Qdrant, Pinecone) and cross-encoder rerankers.
- Building agentic memory systems (short-term, long-term semantic, episodic) using Mem0 or vector stores.
- Implementing cost optimization with provider-native Context Caching (Gemini
cachedContent, Anthropic ephemeral prompt cache, OpenAI prefix cache).
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.
- 4d ago Changed · +1 lines · +78 tokens per session 3ecdab9d90b8
- 5d ago Changed · +2 lines · -78 tokens per session 499aa661cee4
- 8d ago Changed a5c1badbce8c
- 17d ago Changed 08e7fdae9aba
- 22d ago Changed · +2 lines cb0880294511
- 23d ago Changed efe643d7a214
- 25d ago Changed · -6 lines · +61 tokens per session 13a723100d14
- 27d ago Changed · +6 lines · -61 tokens per session 56fa85789fb5
- 1mo ago Changed · +135 lines 6001c34eee5c
- 1mo ago First seen · 195 lines · 78 tokens per session scan A 4181249df79e
ai-llm-integration-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (113 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 6,656 once invoked, about $0.0003 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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