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discovering-gcp-data-assets

Finds and inspects data assets within Google Cloud. Relevant when any of the following conditions are true: 1. The user request involves finding, exploring, or inspecting data assets in Google Cloud, such as: - BigQuery datasets, tables, or views - BigLake catalog or tables - Spanner instances, databases or tables - etc. 2. You need to retrieve the schema, metadata, or governance policies for a GCP data asset. 3. You have a keyword or topic (e.g., "sales data") but lack the specific table or resource ID. 4. You are attempting to find data using `bq ls`, as this skill offers a superior approach. Don't use when: - Assets are outside Google Cloud

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a highly actionable, well-sequenced discovery workflow with concrete commands, explicit stop rules, fallbacks, and error-recovery guidance. Its weaknesses are redundancy (loop-breaking rules stated in three places, heavy MUST repetition) and the absence of any progressive-disclosure split despite reference-grade material like the full search-syntax table being inlined.

DimensionReasoningScore

Conciseness

The body is mostly operational specificity Claude would not know (search syntax, label vs labels, singular keywords), but the loop-breaking guidance is repeated three times ("Avoid Infinite Loops" in Step 3, the "Stop Rule" under NOT_FOUND, and "Breaking the Research Loop" in Troubleshooting) and the known-ID rule is restated in both Step 2 and Step 3. This matches anchor 3 (mostly efficient but could be tightened) rather than anchor 4, where over-explanation would be only minor.

3 / 5

Actionability

Fully executable, copy-paste ready commands for both modes ("gcloud dataplex entries search \"<NATURAL_LANGUAGE_QUERY>\" --project=\"<PROJECT_ID>\" --semantic-search --limit=50", "gcloud dataplex context lookup --resources=\"<FULL_ENTRY_NAME>\"") plus named MCP tools, a complete operator syntax reference, and specific error-message fixes. This matches anchor 5: specific commands cover the common cases.

5 / 5

Workflow Clarity

A clearly sequenced four-step workflow with explicit checkpoints and feedback loops: "Variation Rule: You may try AT MOST 3 variations", "Stop Rule: ...STOP and inform the user", fallbacks to native tools, and completion criteria after each command. All checkpoints are present, matching anchor 5; the one defect is a trivial cross-reference typo ("Proceed to Step 3" should read Step 4), which removes no checkpoint, so anchor 4 does not fit.

5 / 5

Progressive Disclosure

No bundle files exist, so all content is inline in a well-structured body (numbered steps, IMPORTANT/TIP callouts, a cause/fix Troubleshooting section). At roughly 230 lines, the operator syntax reference and troubleshooting sections are plausible candidates for a separate references file, which keeps this at anchor 4 (good structure, minor organization gaps) rather than anchor 5, and it is clearly above anchor 3 since nothing is buried or clearly misplaced.

4 / 5

Total

17

/

20

Passed

Description

82%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly and explicitly states both what the skill does and when to use it, with concrete trigger conditions and a don't-use boundary. Its main weaknesses are the second-person phrasing in the trigger conditions and a few missing natural trigger terms such as "Dataplex" or "data catalog".

DimensionReasoningScore

Specificity

The description names several concrete actions ("Finds and inspects data assets", "retrieve the schema, metadata, or governance policies") with specific asset types (BigQuery, BigLake, Spanner), which fits anchor 4, but the when-clauses use second person ("You need to retrieve", "You have a keyword", "You are attempting"), which the rubric penalizes by reducing specificity by 1 per the third-person-voice guideline. It is not anchor 2 because the actions go well beyond a bare domain statement.

3 / 5

Completeness

Explicitly answers both questions: "Finds and inspects data assets within Google Cloud" (what) and "Relevant when any of the following conditions are true:" with four numbered trigger conditions plus a "Don't use when" exclusion (when). Both are concrete and explicit, matching anchor 5 rather than anchor 4 where the when-clause is only partially specific.

5 / 5

Trigger Term Quality

Good natural-language trigger coverage: "finding, exploring, or inspecting data assets", "sales data", "bq ls", and named systems like BigQuery and Spanner. A few natural terms users would say are missing ("Dataplex" itself, "data catalog", "data discovery"), so it falls short of the comprehensive synonym coverage of anchor 5 but is clearly above the partial coverage of anchor 3.

4 / 5

Distinctiveness Conflict Risk

A clear niche (GCP data-asset discovery) with distinct named triggers and an explicit negative boundary ("Don't use when: Assets are outside Google Cloud"), giving minimal conflict risk consistent with anchor 5 rather than the minor-overlap framing of anchor 4.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

saski/arnesto

Path

.agents/skills/discovering-gcp-data-assets/SKILL.md

Reviewed

Table of Contents

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