What is genai-hub-tabular-orchestration?

A guide for managing SAP AI Core tabular data resources and sending tabular prediction requests. Tabular data is information arranged in rows and columns, such as a spreadsheet or database table.

Use it to create or manage data connections, CSV, Parquet, or Delta table artifacts, scenario configurations, and prediction requests through SAP AI Core.

It ensures the required data destination, queryable table, and named artifact bundle exist before prediction requests are sent. It also adds confirmation rules around changes and prevents credentials from being logged.

genai-hub-tabular-orchestration — Skill for Claude Code by SAP

About the project

SAP/ai-skills-library is a catalogue for discovering, filtering, and installing AI skills that support SAP digital assistants. SAP users and contributors can install listed skills with the skills CLI across compatible coding agents, while skill authors keep their implementations in their own public repositories and register them with the library. The catalogue contains five of the library's skills and its plugin.

SAP/ai-skills-library · 81 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/SAP/ai-skills-library

agentmods

npx agentmods add skills/sap/ai-skills-library/genai-hub-tabular-orchestration

Made for: Claude Code.

Or install aicore-skills, the plugin that ships this one along with the rest of its 4 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/sap/ai-skills-library/genai-hub-tabular-orchestration/github.svg)](https://agentmods.dev/skills/sap/ai-skills-library/genai-hub-tabular-orchestration)

Your own site

<a href="https://agentmods.dev/skills/sap/ai-skills-library/genai-hub-tabular-orchestration"><img src="https://agentmods.dev/badge/skills/sap/ai-skills-library/genai-hub-tabular-orchestration/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/sap/ai-skills-library/genai-hub-tabular-orchestration"><img src="https://agentmods.dev/badge/skills/sap/ai-skills-library/genai-hub-tabular-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 253 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 5,035 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00253	$0.05035
Opus 5.5	$0.00101	$0.02014
Sonnet 5.5	$0.00051	$0.01007
Haiku 4.5	$0.00025	$0.00504

Measured 10d ago against content hash 94b80c3381f7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

genai-hub-tabular-orchestration 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.

The scan reads SKILL.md. This mod also ships 20 executable files (scripts/create_data_destination.py, scripts/create_scenario_config.py, scripts/create_tabular_artifact.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/aicore-skills/skills/genai-hub-tabular-orchestration/SKILL.md · 497 lines

How it starts

The opening of the file, as written. The whole thing — 497 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Rules

Run all scripts from the skill root with uv run scripts/<name>.py.
Never print or log credential values — the SDK reads from the environment.
Always verify TCR prerequisites (data destination → tabular artifact → scenario configuration) are in place before sending a prediction request.
rows and columns are mutually exclusive in a prediction request — never include both.
For credential or auth errors, invoke aicore-admin-resources first.
Mutating scripts require --confirm; always pass it explicitly for delete operations.
Omit --resource-group when the user doesn't specify one; the SDK default is used.
Before issuing any btp update services/instance command: (a) run btp get services/instance --id <id> to fetch the full current parameters block, (b) show the user the complete current parameters and the exact diff — what is being added, changed, or removed — along with the full resulting payload that will be sent, and (c) require the user to explicitly confirm the shown parameters and the proposed change before proceeding. Do not issue btp update services/instance under any circumstance until the user has confirmed in that same turn.

Concepts

The Context Registry (TCR) exposes structured data to AI Core scenarios through three layered resources:

Data Destination  →  Tabular Artifact  →  Scenario Configuration
(HDL connection)      (file + schema)       (named bundle of TAs)

Data Destination: stores connection credentials for an HDL (HANA Data Lake) endpoint.
Tabular Artifact (TA): points to a specific file on a data destination, with schema metadata (CSN). Supports CSV, Parquet, and Delta formats. Schema can be provided inline (DOCUMENT), referenced via a path (REFERENCE), or auto-derived (AUTO).
Scenario Configuration: a named, reusable list of tabular artifact names — consumed by AI Core scenarios at inference time.

All resources are scoped to a resource group (AI-Resource-Group header). Use the aicore-admin-resources skill to verify credentials and get the active resource group first.

Read the full file on GitHub · 497 lines

Files

What ships with it

23 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.

Changes

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 · 497 lines · 253 tokens per session scan A 94b80c3381f7

Subscribe to this mod's changes

genai-hub-tabular-orchestration is a skill published in the GitHub repository SAP/ai-skills-library (81 stars, last pushed 11d ago), licensed Apache-2.0. It adds 253 tokens to every session and 5,035 once invoked, about $0.0010 per session on Opus 5.5. 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 2026-10-06.

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