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/ibm/data-intelligence-mcp-server/data-product-creationnpx skills add IBM/data-intelligence-mcp-server --skill data-product-creationgit clone --depth 1 https://github.com/IBM/data-intelligence-mcp-serverWrote 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/data-intelligence-mcp-server/data-product-creation)<a href="https://agentmods.dev/skills/ibm/data-intelligence-mcp-server/data-product-creation"><img src="https://agentmods.dev/badge/skills/ibm/data-intelligence-mcp-server/data-product-creation.svg" alt="Measured on agentmods" 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 | $0.00153 | $0.06042 |
| Opus 5 | $0.00077 | $0.03021 |
| Sonnet 5 | $0.00031 | $0.01208 |
| Haiku 4.5 | $0.00015 | $0.00604 |
Grade A, and why
data-product-creation 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 — 810 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Product Creation Skill (File-Based Specification)
You are helping a user create and publish a high-quality data product in IBM watsonx.data intelligence (Data Product Hub) using an Infrastructure-as-Code approach. Instead of making iterative API calls, you will generate specification files that define the data product, allow the user to review and refine them, then batch-submit to DPH.
Core Paradigm: File-Based Specification
File-Based Approach (Use This):
Human ↔ LLM ↔ Workspace Files → MCP Tools (5-7 batch API calls)
Benefits:
- Version Control: Files can be committed to Git, tracked, branched, and merged
- Collaboration: Multiple stakeholders can review and edit specification files
- Reusability: Templates can be created for common data product patterns
- Validation: Files can be validated before any API calls are made
- Transparency: Complete data product definition visible in one place
- Iteration: Refine specifications without touching the DPH server
Workflow Overview
This skill has five phases. Follow them in order:
- Discovery & Requirements (conversational + MCP tools)
- Specification Generation (create files in workspace)
- Iterative Refinement (user reviews/edits files)
- Validation (AI code review of files)
- Publication (batch MCP tool calls)
Optional Calls
WHEN: User asks about available data contracts (e.g., "What data contracts are available?")
CALL: list_data_product_contract_templates tool
WHEN: User asks about available business domains (e.g., "What domains are available?")
CALL: list_data_product_business_domains tool
WHEN: User asks about available delivery methods for a data asset (e.g., "What delivery methods are available for asset_name?")
CALL: find_data_product_delivery_methods_based_on_connection tool
Phase 1 — Discovery & Requirements
Goal: Understand what the user wants to create and check for duplicates.
What ships with it
8 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.
- templates/assets_manifest_asset_based.json 639 B
- templates/assets_manifest_url_based.json 248 B
- templates/contract_custom.json 1.9 KB
- templates/contract_template.json 213 B
- templates/contract_url.json 127 B
- templates/data_product_spec.json 342 B
- templates/delivery_config.json 340 B
- templates/README.md 2.8 KB
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 First seen · 810 lines · 153 tokens per session scan A 29fcfe163f71
data-product-creation is a skill published in the GitHub repository IBM/data-intelligence-mcp-server (19 stars, last pushed 28d ago), licensed Apache-2.0. It adds 153 tokens to every session and 6,042 once invoked, about $0.0008 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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