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/googlecloudplatform/cortex-framework/query_sap_ddicnpx skills add GoogleCloudPlatform/cortex-framework --skill query_sap_ddicgit clone --depth 1 https://github.com/GoogleCloudPlatform/cortex-frameworkWhat 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 | $0.00053 | $0.01568 |
| Opus 5 | $0.00026 | $0.00784 |
| Sonnet 5 | $0.00011 | $0.00314 |
| Haiku 4.5 | $0.00005 | $0.00157 |
Grade A, and why
query-sap-ddic 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 2d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query SAP Data Dictionary (DDIC)
This skill retrieves SAP schema structures, data types, field descriptions, key designations, and check relationships directly from the replicated SAP Data Dictionary (DDIC) metadata tables inside BigQuery.
Using DDIC tables is highly recommended during modeling and design phases as it reflects the actual system metadata synced from the SAP source system.
Prerequisites & Tables Involved
The skill assumes that standard SAP DDIC tables are replicated into your BigQuery raw source dataset:
DD03L: Fields definitions (contains data types, lengths, offsets, and key markings).DD04T: Data Element Texts (contains localized English field descriptions).DD08L: Table Relationships (contains check-table foreign key configurations).DD01L: Domain definitions (contains conversion exits technical details).DD07L: Domain Values (contains allowed domain values and fixed ranges, optional).DD07T: Domain Value Texts (contains localized English descriptions of allowed domain values, optional).
By default, the utility parses cortex-framework-core/config/config.yaml to automatically locate your SAP raw dataset by finding the entry in data.sources where id matches the active foundation data module's dataSourceId (which defaults to sap_raw). If the tables are not found there, you must explicitly override the path.
How to Use
To query the schema of any SAP table (e.g., VBAK or BSEG), run the following script:
cortex-framework-core/.venv/bin/python cortex-framework-core/.agents/skills/query_sap_ddic/scripts/query_sap_ddic.py <table_name> [--config <path>] [--dataset <project_id>.<dataset_id>] [--format {markdown,json,yaml}]
Usage Examples:
- Query VBAK using your active configuration settings (Default Markdown):
cortex-framework-core/.venv/bin/python cortex-framework-core/.agents/skills/query_sap_ddic/scripts/query_sap_ddic.py VBAK
What ships with it
1 file 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.
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.
- 2d ago First seen · 94 lines · 53 tokens per session scan A aeb1164386e3
query-sap-ddic is a skill published in the GitHub repository GoogleCloudPlatform/cortex-framework (10 stars, last pushed 6d ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,568 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 2026-08-31.
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