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 opensearch-vector-searchgit 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/opensearch-vector-search)<a href="https://agentmods.dev/skills/ibm-self-serve-assets/building-blocks/opensearch-vector-search"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/opensearch-vector-search/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/opensearch-vector-search"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/opensearch-vector-search.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 | $0.00093 | $0.01330 |
| Opus 5 | $0.00046 | $0.00665 |
| Sonnet 5 | $0.00019 | $0.00266 |
| Haiku 4.5 | $0.00009 | $0.00133 |
Grade A, and why
opensearch-vector-search 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IBM watsonx.data OpenSearch Vector Search Builder
Purpose
This skill defines the complete workflow for building IBM watsonx.data OpenSearch vector search applications using IBM watsonx.ai embeddings and IBM Cloud Object Storage as the document source. Generates deployable Python 3.12 FastAPI services.
IBM Cloud Product Coverage
| IBM Cloud Product | Usage |
|---|---|
| IBM watsonx.data (OpenSearch) | k-NN vector index, BM25 full-text search, hybrid search |
| IBM watsonx.ai | Embedding generation: ibm/slate-125m-english-rtrvr, ibm/slate-30m-english-rtrvr |
| IBM Cloud Object Storage | Document source bucket with ibm-cos-sdk download |
| IBM Cloud IAM | POST /identity/token (apikey grant) for watsonx.ai auth |
Objective
Transform natural language vector search requirements into deployable services that:
- Create optimised k-NN indexes in IBM watsonx.data OpenSearch
- Generate embeddings using IBM watsonx.ai models
- Ingest documents from IBM COS with chunking via
unstructured - Perform vector, keyword, and hybrid search
- Follow Python 3.12 best practices with Pydantic v2
Rules
- Always use
ibm_watsonx_ai.foundation_models.Embeddingsfor IBM embeddings - IBM watsonx.ai base URL:
https://us-south.ml.cloud.ibm.com(or configured region) - Use
opensearch-pywith SSL/TLS for watsonx.data managed OpenSearch - Default embedding model:
ibm/slate-125m-english-rtrvr(dim=768) - Wrap IBM COS calls with
ibm-cos-sdk(IAM OAuth, not HMAC)
Scope
- IBM watsonx.data OpenSearch k-NN index creation and management
- IBM watsonx.ai embedding generation for ingestion and query
- Document ingestion from IBM COS using
unstructured - k-NN vector search, BM25 keyword search, hybrid search
- Score normalisation and result reranking
Procedure
Phase 1: IBM watsonx.ai Embeddings
from ibm_watsonx_ai import APIClient, Credentials
from ibm_watsonx_ai.foundation_models import Embeddings
embedder = Embeddings(
model_id="ibm/slate-125m-english-rtrvr", # dim=768
credentials=Credentials(url="https://us-south.ml.cloud.ibm.com", api_key=IBM_API_KEY),
project_id=WATSONX_PROJECT_ID,
)
vectors = embedder.embed_documents(["text chunk 1", "text chunk 2"])
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 · 155 lines · 93 tokens per session scan A 01ea3a4d23d9
opensearch-vector-search 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 93 tokens to every session and 1,330 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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