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 ibm-self-serve-assets/building-blocks --skill rag-pipeline-buildergit clone --depth 1 https://github.com/ibm-self-serve-assets/building-blocksWrote 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/ibm-self-serve-assets/building-blocks/rag-pipeline-builder)<a href="https://agentmods.dev/skills/ibm-self-serve-assets/building-blocks/rag-pipeline-builder"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/rag-pipeline-builder/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/ibm-self-serve-assets/building-blocks/rag-pipeline-builder"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/rag-pipeline-builder.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 | $0.00104 | $0.01201 |
| Opus 5 | $0.00052 | $0.00600 |
| Sonnet 5 | $0.00021 | $0.00240 |
| Haiku 4.5 | $0.00010 | $0.00120 |
Grade A, and why
rag-pipeline-builder 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 11d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IBM RAG Pipeline Builder
Purpose
Expert guidance for designing and implementing complete IBM RAG (Retrieval-Augmented Generation) pipelines using IBM Cloud services. Covers every stage from document ingestion to LLM answer generation.
IBM Cloud Product Coverage
| IBM Cloud Product | RAG Stage |
|---|---|
| IBM Cloud Object Storage | Document source |
| IBM watsonx.ai | Embedding generation + LLM generation (Granite, Llama) |
| IBM watsonx.data (Milvus) | Primary vector store for high-scale RAG |
| IBM watsonx.data (OpenSearch) | Vector + BM25 hybrid search |
| IBM DataStax Astra DB (HCD) | Alternative vector store for global distribution |
Rules
- Preferred embedding model:
ibm/slate-125m-english-rtrvr(dim=768) - Preferred generation models:
ibm/granite-3-8b-instruct,meta-llama/llama-3-3-70b-instruct - Use
ibm_watsonx_ai.foundation_models.Embeddingsfor embedding generation - Use
langchain_ibm.WatsonxLLMoribm_watsonx_aiinference API for generation - Chunking default:
RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=128) - Hybrid search: always combine vector + BM25 (weight: 70% vector, 30% BM25)
- Reranking: use
ibm/slate-125m-english-rtrvrcross-encoder or BM25 score fusion
Scope
- RAG architecture selection (Milvus vs OpenSearch vs AstraDB)
- Chunking strategy optimisation for different document types
- IBM watsonx.ai embedding and generation integration
- Hybrid search design and score normalisation
- RAG evaluation with RAGAS metrics
- MCP server design for RAG tool exposure
RAG Architecture Decision Matrix
| Criterion | Milvus | OpenSearch | AstraDB |
|---|---|---|---|
| Scale | Billion-scale | Million-scale | Global scale |
| Hybrid search | Manual | Native BM25 | Manual |
| Deployment | IBM watsonx.data | IBM watsonx.data | IBM HCD SaaS |
| Best for | Large-scale RAG | Enterprise search + RAG | Global SaaS RAG |
Procedure
Phase 1: Embedding Generation
from ibm_watsonx_ai import Credentials
from ibm_watsonx_ai.foundation_models import Embeddings
embedder = Embeddings(
model_id="ibm/slate-125m-english-rtrvr",
credentials=Credentials(url="https://us-south.ml.cloud.ibm.com", api_key=IBM_API_KEY),
project_id=WATSONX_PROJECT_ID,
)
query_vector = embedder.embed_query(user_question)
doc_vectors = embedder.embed_documents(chunks)
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.
- 11d ago First seen · 121 lines · 104 tokens per session scan A 9f9475de8e7f
rag-pipeline-builder is a skill published in the GitHub repository ibm-self-serve-assets/building-blocks (24 stars, last pushed 2d ago), licensed Apache-2.0. It adds 104 tokens to every session and 1,201 once invoked, about $0.0005 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 2026-08-30.
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