knowledge_catalog_governance
Skill397 starsChanged 11 days agoApache-2.0View on GitHub
Enforces data governance by verifying Knowledge Catalog Aspects before recommending BigQuery tables.
In this repository79 other files it gives its agents.
- agents/newsroom-harness/AGENTS.mdAGENTS.md
- codelabs/ai-toolkit-lab-2/hackathon-judge/frontend/.agents/skills/vercel-composition-patterns/AGENTS.mdAGENTS.md
- codelabs/ai-toolkit-lab-2/hackathon-judge/frontend/.agents/skills/vercel-react-best-practices/AGENTS.mdAGENTS.md
- codelabs/bwg002-devrel-2/AGENTS.mdAGENTS.md
- calendar-exportSkill
- session-notesSkill
- calendar-exportSkill
- session-notesSkill
- calendar-exportSkill
- session-notesSkill
- calendar-exportSkill
- session-notesSkill
- compare-subagent-mcp-visibilitySkill
- batch-executing-test-suitesSkill
- circuit-breaker-reporterSkill
- generating-mock-imagesSkill
- making-forms-demoSkill
- multi-step-workflow-reporterSkill
- auditing-inventorySkill
- newsroom-harnessSkill
- verify-sourcesSkill
- write-bulletinSkill
- repo-walkthroughSkill
- go-project-setupSkill
- latest-software-versionSkill
- video-descriptionSkill
- adk-memory-bank-initializerSkill
- agent-containerizerSkill
- cloud-run-agent-architectSkill
- gcp-agent-eval-engine-runnerSkill
- gcp-agent-eval-metric-configuratorSkill
- gcp-agent-golden-dataset-builderSkill
- gcp-agent-model-armor-shieldSkill
- gcp-agent-safety-gatekeeperSkill
- gcp-agent-sdp-template-factorySkill
- gcp-agent-shadow-deployerSkill
- gcp-agent-tool-trajectory-evaluatorSkill
- gcp-production-secret-handlerSkill
- mcp-connector-generatorSkill
- go-architectSkill
- go-backend-devSkill
- go-reviewerSkill
- go-test-expertSkill
- vercel-composition-patternsSkill
- vercel-react-best-practicesSkill
- lab-helperSkill
- start-frontendSkill
- brand-guidelinesSkill
- brand-guidelinesSkill
- banana-breadSkill
- cheese-breadSkill
- drawingsSkill
- paintingsSkill
- photographySkill
- ticket-triageSkill
- adding-easter-eggSkill
- building-platformer-gamesSkill
- creating-a-gameSkill
- ensuring-arcade-visualsSkill
- handling-user-inputSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 2 are distinctive, and 7 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
7
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 21
If these are not already present in your system context or provided by the user, you must discover them automatically:
- Very specific: line 22
Call search_entries with a simple query (e.g., official-data-product-spec or type:table) and a pageSize of 1.
- Very specific: line 27
Use the search_entries tool to find tables that match the user's request.
- Distinctive: line 32
Use the Project ID and Location discovered in Phase 1.
- Very specific: line 33
Do not use prefixes like projectid: or type=table when performing aspect-based searches.
- Very specific: line 50
Call lookup_entry with the entry path and set view to 4 (ALL) to verify:
- Very specific: line 51
Certification: Check if is_certified is true.
- Very specific: line 52
Data Tiering: Check the product_tier (e.g., GOLD_CRITICAL, SILVER_STANDARD, BRONZE_ADHOC).
- Very specific: line 53
Usage Scope: Verify the usage_scope (e.g., INTERNAL_ONLY, EXTERNAL_READY) matches the user's target audience.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Knowledge Catalog Governance Skill
This skill enables the agent to act as a Data Governance Specialist. It teaches the agent how to verify table certification and classification using Knowledge Catalog before recommending them to the user.
Required Capabilities
This skill requires access to the following tools:
search_entries: To find tables matching specific aspect values.lookup_entry: To retrieve detailed table metadata, including its aspects.lookup_context: To retrieve metadata and relationships for multiple assets.
Governance Workflow
The agent must follow a strict verification process before recommending or querying any data asset.
Phase 1: Environment Discovery (If needed)
To search by aspect values, you need the active Google Cloud Project ID and Location (Region). If these are not already present in your system context or provided by the user, you must discover them automatically:
- Call
search_entrieswith a simple query (e.g.,official-data-product-specortype:table) and apageSizeof1. - Inspect the returned entry's
namefield (format:projects/{project_id}/locations/{location}/entryGroups/...). - Extract the
{project_id}and{location}. These are your active environment parameters.
Phase 2: Search and Filter
Use the search_entries tool to find tables that match the user's request. You must filter by the governance aspect official-data-product-spec.
Tool Call Parameters:
query: Construct a query string matching the aspect values.- Format:
[PROJECT_ID].[LOCATION].official-data-product-spec.[FIELD_NAME]=[VALUE] - Use the Project ID and Location discovered in Phase 1.
- Do not use prefixes like
projectid:ortype=tablewhen performing aspect-based searches.
- Format:
scope: Set this toprojects/[PROJECT_ID]to restrict the search space.
Examples:
- To find certified financial data:
{ "name": "search_entries", "arguments": { "query": "my-project.us-central1.official-data-product-spec.data_domain=FINANCE my-project.us-central1.official-data-product-spec.is_certified=true", "scope": "projects/my-project" } }
Phase 3: Detailed Verification
Extract the resource name (entry path) for the candidate tables from the search results.
Call lookup_entry with the entry path and set view to 4 (ALL) to verify:
- Certification: Check if
is_certifiedistrue. Iffalseor missing, the table is untrusted. - Data Tiering: Check the
product_tier(e.g.,GOLD_CRITICAL,SILVER_STANDARD,BRONZE_ADHOC). - Usage Scope: Verify the
usage_scope(e.g.,INTERNAL_ONLY,EXTERNAL_READY) matches the user's target audience.
Phase 4: Formulate Response
Synthesize your final answer explaining WHY you chose this table based on its governance tags (Aspects). Do not expose the raw Knowledge Catalog search query or entry paths to the user.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

