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/data-intelligence-mcp-server --skill onboard-and-enrichgit 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/onboard-and-enrich)<a href="https://agentmods.dev/skills/ibm/data-intelligence-mcp-server/onboard-and-enrich"><img src="https://agentmods.dev/badge/skills/ibm/data-intelligence-mcp-server/onboard-and-enrich/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/data-intelligence-mcp-server/onboard-and-enrich"><img src="https://agentmods.dev/badge/skills/ibm/data-intelligence-mcp-server/onboard-and-enrich.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00063 | $0.02677 |
| Opus 5 | $0.00032 | $0.01339 |
| Sonnet 5 | $0.00013 | $0.00535 |
| Haiku 4.5 | $0.00006 | $0.00268 |
Grade A, and why
onboard-and-enrich 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 10d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Cataloging and Metadata Enrichment Guide
Overview
Understand the user prompt to identify if the user wants to onboard data, catalog data, enrich data or import new data and use this skill to guide them through the workflow in all those cases. Use this skill any time the user mentions onboarding data, cataloging data, enriching metadata, importing schemas, setting up a data project, or connecting to a data source — even if they don't use those exact words. Depending on the user request, users can start from any phase in the workflow, not necessarily from the beginning. Example user queries: "Catalog our Postgres sales database", "I want to enrich the data I already imported last week", "Set up a new project for the Finance team and import their Oracle data", "Onboard data from DB2 connection".
Phase 0: Intent Detection
- Always start by determining the user's intent based on their query. Read the user's request and decide which phase to start from.
- If the user provides a project name in their query, use Phase 1 to check if the project exists, and directly proceed to the next phase.
- If the user provides a connection name in their query, use Phase 2 to check if the connection exists in the project provided, and directly proceed to the next phase.
- If the user query extablishes that the project and connection already exist, start from Phase 3 or Phase 4 depending on the user's intent.
- If the user query establishes that the data is already imported, start from Phase 4 to enrich the metadata.
Examples of User Queries and Corresponding Phases:
- For user query: "Catalog our Postgres sales database", in this case the user wants to catalog existing data, so start from Phase 1 to find the project where the Postgres connection is already configured, proceed to Phase 2 to find the connection, and proceed to Phase 3 to import the metadata, and then to Phase 4 for metadata enrichment.
- For user query: "I want to enrich the data I already imported last week", in this case the user wants to enrich already imported data, so start at Phase 1 to find the project where the data was imported, proceed to Phase 4 to enrich the metadata.
- For user query: "Set up a new project for the Finance team and import their Oracle data", in this case the user wants to set up a new project and import data, so start at Phase 1 to create a new project, proceed to Phase 2 to configure the Oracle connection, and proceed to Phase 3 to import the metadata, and then to Phase 4 for metadata enrichment.
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.
- 10d ago First seen · 151 lines · 63 tokens per session scan A 96cf81c115dc
onboard-and-enrich is a skill published in the GitHub repository IBM/data-intelligence-mcp-server (19 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,677 once invoked, about $0.0003 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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