DB
oracle/skills
Oracle Database guidance for SQL, PL/SQL, SQLcl, ORDS, Oracle Vector SDK, administration, app development, performance, security, migrations, and agent-safe database workflows.
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .claude/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .claude/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydbType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .agents/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .agents/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .cursor/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .cursor/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/google-cloud-solution-hybrid-search-alloydb--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .gemini/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .gemini/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydbInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .github/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .github/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .opencode/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "google-cloud-solution-hybrid-search-alloydb" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb into .opencode/skills/google-cloud-solution-hybrid-search-alloydb/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-hybrid-search-alloydb", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
google-cloud-solution-hybrid-search-alloydbDiscovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.
Google Cloud Solution Hybrid Search Alloydb is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for…
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `assets/output-template.md`, `references/design-recommendations.md` and `references/product-mapping.md`).
It sits in Databases, covering Retrieval-augmented generation and Vector databases. It works with Google Cloud and SQL. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 5120a76. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gcloudterraformFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
developers.google.comdeveloperknowledge.googleapis.comgithub.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Google Cloud Solution Hybrid Search Alloydb loads about 4k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 164 tokens; SKILL.md has 1,874 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from google/skills at commit 5120a76, republished under its Apache-2.0 licence (© google). 1,874 words, ~3,984 tokens.
.claude/skills/google-cloud-solution-hybrid-search-alloydb/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.This skill provides a workflow to design and implement secure, low-latency, and high-accuracy hybrid search solutions combining structured dataset filtering, vector search indexing, faceted metadata filtering, semantic reranking, recall evaluation, in-database AI validation, database abstraction layers, and serverless application hosting.
The workflow consists of the following phases:
Important notes about the workflow:
Design Recommendations for the required guidance. If the guidance does not provide the required information, you MUST ground the generated content by using the following resources:
Google Developer Knowledge MCP server: https://developers.google.com/knowledge/mcp.md.txt
developerknowledge:search_documentsdeveloperknowledge:get_documentsdeveloperknowledge:answer_queryRelevant skills from https://github.com/google/skills
Official Google Cloud documentation in Related Guidance
When generating solution designs, architecture diagrams, and documentation, check the latest Google Cloud documentation for the most up-to-date product names. The table below provides examples of name mappings to be aware of. Note that underlying APIs, Terraform resources, and IAM roles may retain their legacy identifiers.
<table>
<thead>
<tr>
<th>Legacy Name</th>
<th>Updated Name</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>Vertex AI</td>
<td>Gemini Enterprise Agent Platform</td>
<td>Gemini Enterprise Agent Platform can be shortened to Agent Platform after first instance</td>
</tr>
<tr>
<td>Vertex AI Embedding</td>
<td>Text embedding on Gemini Enterprise Agent Platform</td>
<td>This refers to the text embedding models available on Gemini Enterprise Agent Platform</td>
</tr>
<tr>
<td>Vertex AI Matching Engine</td>
<td>Vector Search</td>
<td></td>
</tr>
</tbody>
</table>
In this phase, you must gather detailed requirements related to the hybrid search workload that the user wants to design and deploy in Google Cloud.
Acknowledge provided requirements: If the user's prompt already contains some requirements (functional or non-functional, such as catalog size, search modalities, faceted attributes, or latency targets), you MUST explicitly acknowledge and restate all of these requirements in your response. Do NOT ask the user to describe or re-describe any requirements that they have already provided in the prompt.
Complete the following steps strictly in the specified order:
Step 1: Ask the user to describe the functional requirements of the
workload, including catalog dataset details (e.g., e-commerce apparel, retail
products, patent database), search modalities (natural language text, visual
search, attribute filters), metadata attributes for faceted filtering (e.g.,
category, sub_category, color, gender, price), and quality checks
(reranking, LLM validation).
Step 2: You MUST explicitly ask the user to describe ALL of the following six categories of non-functional requirements. You need this information because each category represents a critical architectural pillar, and neglecting any of them can result in a solution that is insecure, unreliable, or inefficient (do NOT omit any of them):
Step 3: Ask the user whether the workload currently runs on other cloud providers or on-premises.
Step 4: Ask the user to describe dependencies, if any, on other workloads, products, or tools (e.g., existing inventory databases, ERP systems, application runtime languages like Java or Python).
Step 5: Review the input that the user has provided so far, and check whether there are any ambiguities, conflicts, or contradictions in the functional requirements, non-functional requirements, and dependencies. You MUST compare all requirements against each other to identify any conflicts.
If you identify any ambiguities, conflicts, or contradictions in the requirements that the user has provided, you MUST do the following for each ambiguity, conflict, or contradiction:
Critical: Until all the ambiguities and contradictions that you identify are resolved according to the preceding guidance, you must NOT recommend or generate any architecture design or Google Cloud product recommendations.
Step 6: Summarize the functional and non-functional requirements provided by the user into a consolidated requirements summary.
Step 7: Present the generated requirements summary to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Phase 2.
Important: STOP, DON'T proceed to generate architecture diagram, architecture description or product recommendations until you have confirmed the generated requirements summary and resolved all ambiguities and contradictions in this phase.
Step 1: Recommend products and features that are appropriate for each component of the user's workload, prioritizing Google Cloud products.
Important: The Google Cloud products and features that you recommend MUST be consistent with the guidance in Product Mapping.
Step 4: Present the generated product recommendations to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.2.
Important: STOP, DON'T proceed to generate architecture diagram until you have confirmed the generated product recommendations with the user.
Step 1: Generate an architecture diagram in the Mermaid format: https://github.com/mermaid-js/mermaid.
The diagram must show the data flows and request flows across the components of the architecture, based on the gathered requirements and product recommendations. The diagram MUST explicitly show both the ingestion pipeline and serving pipeline.
The following is an example of the data flows and request flows that the architecture diagram should show:
apparels) -> B-Tree Indexes on Facets -> Text embedding
(text-embedding-005) -> ScaNN Vector Index.ai.rank Reranker -> Gemini Pro
ai.generate Quality Validation -> Validated Results -> User Browser.Step 2: Generate a description that explains the purpose of each component, the relationships between the components, and the task flow or data flow.
Step 3: Present the generated architecture diagram and description to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.3.
Important: STOP, DON'T proceed to generate design recommendations until you have confirmed the generated architecture description with the user.
Step 1: Generate design recommendations and best practices to optimally configure each component in the architecture based on the workload requirements.
Important:
google-cloud-waf-securitygoogle-cloud-waf-reliabilitygoogle-cloud-waf-cost-optimizationgoogle-cloud-waf-operational-excellencegoogle-cloud-waf-performance-optimizationgoogle-cloud-waf-sustainabilityStep 2: Present the generated recommendations to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.4.
Important: STOP, DON'T proceed to generate deployment guidance until you have confirmed the design recommendations with the user.
Step 1: Generate guidance to deploy the solution, including the following:
google_ml_integration,
alloydb_scan), tables, B-Tree indexes, ScaNN vector indexes, hybrid search
SQL, and Gemini validation CTEs.gcloud run deploy {app_name}).gcloud CLI commands to create required infrastructure.Important: The deployment guidance that you generate MUST be consistent with the guidance in the following resources:
Step 2: Present the generated deployment guidance to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Phase 3.
Important: STOP, DON'T proceed to generate solution validation until you have confirmed the deployment guidance with the user.
terraform plan or (where supported) gcloud ... --dry-run).curl, ping, or gcloud)
and provide them to the user to execute, to test live endpoint
reachability, networking paths, and load balancer routing.solution-architecture-guide.md,
based on the template in Output Template.© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (references, assets) in skills/cloud/google-cloud-solution-hybrid-search-alloydb of google/skills.
Open the folder on GitHubat commit 5120a76
Google Cloud Solution Hybrid Search Alloydb next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Google Cloud Solution Hybrid Search Alloydb this skillgoogle/skills | 21k | — | ~4k | Automated safety check: Pass | Apache-2.0 | |
| DBoracle/skills | 876 | — | ~1.4k | Automated safety check: Pass | UPL-1.0 | |
| Aliyun Opensearch Searchcinience/alicloud-skills | 397 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Retail Product Search Agentgoogle/adk-recipes | 10k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Neon Postgresneondatabase/agent-skills | 100 | — | ~4.1k | Automated safety check: Notes | Apache-2.0 | |
| Cortexdbliliang-cn/cortexdb | 274 | — | ~18k | Automated safety check: Warn | MIT |
oracle/skills
Oracle Database guidance for SQL, PL/SQL, SQLcl, ORDS, Oracle Vector SDK, administration, app development, performance, security, migrations, and agent-safe database workflows.
cinience/alicloud-skills
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google/adk-recipes
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neondatabase/agent-skills
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liliang-cn/cortexdb
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Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.
google/skills
Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services.
google/skills
Manages Google Cloud Privileged Access Manager entitlements and grants: create and edit entitlements, request temporary access, and approve or deny pending grants.
google/skills
Writes Terraform alerting policies for AI agents that emit OpenTelemetry metrics, covering reliability, cost, safety, security and quality signals on Google Cloud.
google/skills
Deploys open models or custom weights from Model Garden to Agent Platform endpoints, checks deployment status and cleans up endpoints, confirming before any change.
google/skills
Searches, manages and scaffolds skills in the Gemini Enterprise Agent Platform Skill Registry using bundled Python scripts and Google Cloud credentials.
google/skills
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Works with
Categories
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Google Cloud Solution Hybrid Search Alloydb is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.
Google Cloud Solution Hybrid Search Alloydb fits situations like: users need vector search combined with structured SQL filtering; faceted attributes; semantic reranking; in-database AI validation.
Run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a claude-code`. Or copy the skill folder (skills/cloud/google-cloud-solution-hybrid-search-alloydb in google/skills) into .claude/skills/google-cloud-solution-hybrid-search-alloydb in your project. Claude Code loads it when a task matches its description.
Run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a codex`. Or copy the skill folder (skills/cloud/google-cloud-solution-hybrid-search-alloydb in google/skills) into .agents/skills/google-cloud-solution-hybrid-search-alloydb in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-cloud-solution-hybrid-search-alloydb, .gemini/skills/google-cloud-solution-hybrid-search-alloydb, .github/skills/google-cloud-solution-hybrid-search-alloydb and .opencode/skills/google-cloud-solution-hybrid-search-alloydb in your project.
Going by SKILL.md and its folder, Google Cloud Solution Hybrid Search Alloydb needs the command-line tools its instructions call (gcloud and terraform).
SKILL.md names 3 domains. As links in the text: developers.google.com, developerknowledge.googleapis.com and github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Google Cloud Solution Hybrid Search Alloydb is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Google Cloud Solution Hybrid Search Alloydb: DB (oracle/skills, 876 stars), Aliyun Opensearch Search (cinience/alicloud-skills, 397 stars), Retail Product Search Agent (google/adk-recipes, 10k stars) and Neon Postgres (neondatabase/agent-skills, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,069 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 9, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.