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 agentmods add skills/graycodeai/starling/mdc-langchain-rag-application-developmentnpx skills add GrayCodeAI/starling --skill mdc-langchain-rag-application-developmentgit clone --depth 1 https://github.com/GrayCodeAI/starlingWhat 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 | $0.00033 | $0.00070 |
| Opus 5 | $0.00016 | $0.00035 |
| Sonnet 5 | $0.00007 | $0.00014 |
| Haiku 4.5 | $0.00003 | $0.00007 |
Grade A, and why
mdc-langchain-rag-application-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 2d 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.
What it actually says
You are familiar with create RAG applications using Langchain and are aware of its latest features.
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.
- 2d ago First seen · 8 lines · 33 tokens per session scan A 15e50e3e4268
mdc-langchain-rag-application-development is a skill published in the GitHub repository GrayCodeAI/starling (2 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 70 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other skills, from other repositories
rag-assistant
本地知识库助手：当用户询问已索引文档的内容、对比多份资料、或要求基于私有文档回答问题时使用。.
rag-prep
Prepare and clean a document so it works well in a RAG source (NotebookLM, Claude Projects/Cowork, any retrieval backend). Use when the user wants to "подготовить документ под RAG / NotebookLM", "почистить исходник для базы знаний", "разбить на чанки / структурировать под retrieval", "prep doc for RAG", "make this…
obsidian
Compatibility slash-command alias for the Vault Operations phase of obsidian-memory-wiki. Install/load obsidian-memory-wiki as the canonical parent skill.
orchestrate-evidence-retrieval-expert
Design and defend the evidence column. Use when building retrieval or considering a ranker change. Covers the ceiling analysis that tells you whether a gain is possible at all, and why the fashionable option often loses.
rag-system-design
Design, evaluate, and operate retrieval-augmented generation systems.
ask-ai
Configure the Ask AI (RAG-powered AI chat) in @farming-labs/docs. Use when enabling AI chat, setting mode (search vs floating), floatingStyle (panel, modal, popover, full-modal), position, providers, models, suggestedQuestions, apiKey, systemPrompt, or maxResults. Covers Next.js, SvelteKit, Astro, Nuxt and env vars.