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 gemini-cli-extensions/data-agent-kit-starter-pack --skill discovering-gcp-data-assetsgit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote 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/gemini-cli-extensions/data-agent-kit-starter-pack/discovering-gcp-data-assets)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/discovering-gcp-data-assets"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/discovering-gcp-data-assets.svg" alt="Measured on agentmods" 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 | $0.00178 | $0.02488 |
| Opus 5 | $0.00089 | $0.01244 |
| Sonnet 5 | $0.00036 | $0.00498 |
| Haiku 4.5 | $0.00018 | $0.00249 |
Grade A, and why
discovering-gcp-data-assets 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- discovering-gcp-data-assets — 100% identical, 31 lines differ
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Step 1: Prioritize Assets from the Conversation
If the asset was created or mentioned earlier in the same conversation, then proceed with that asset instead of searching. Skip steps 2, 3, and 4.
Step 2: Handle Public Datasets or Proceed to Search
Dataplex Lookup Context provides the richest metadata for data assets. You MUST prioritize using it for all Google Cloud assets, even if you already know their IDs.
- Public Datasets (Direct Inspection): If the requested asset belongs to
the
bigquery-public-dataproject, Dataplex Lookup Context will fail. You MUST skip Steps 3 and 4 and inspect the table directly using thebqCLI or BigQuery MCP tools instead. - All Other Assets (Proceed to Step 3): For all other BigQuery, Cloud Storage, Spanner, BigLake Iceberg or general GCP data assets (whether their IDs are known or missing), you MUST proceed to Step 3 to search the Dataplex catalog and obtain their full Entry Name.
Step 3: Execute Discovery Search
You MUST use the Dataplex search command to discover assets and retrieve their
full projects/... entry names. This step is required even if you already know
the asset's short ID (e.g., my_dataset.my_table), because Step 4 strictly
requires the full entry name.
[!IMPORTANT]
The
--projectparameter MUST ALWAYS be provided. This project_id is used to attribute the search only and does NOT restrict the search scope. The project must have the dataplex API enabled and user must have thedataplex.entries.getpermissions.
A. Semantic Search (Natural Language Intent)
Use this when the user describes the meaning or intent of the data (e.g., "Find Q4 product sales data").
Use the search_entries MCP tool
OR
gcloud dataplex entries search "<NATURAL_LANGUAGE_QUERY>" \
--project="<PROJECT_ID>" \
--semantic-search \
--limit=50
B. Keyword Search (Technical Strings)
Use this for exact keyword matches or technical strings (e.g., name:order_v2).
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.
- 8d ago First seen · 241 lines · 178 tokens per session scan A 558a0e0738a9
discovering-gcp-data-assets is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (180 stars, last pushed today), licensed Apache-2.0. It adds 178 tokens to every session and 2,488 once invoked, about $0.0009 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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