데이터·AI 스킬

Find and manage agent skills in the Gemini Enterprise Agent Platform Skill Registry, a catalog of available capabilities. The skill supports searching, uploading, updating, deleting, inspecting revision histories, checking operation status, and creating local starter files.

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• Search for skills that provide a needed capability
• Upload or update skills in the registry
• Inspect skill details and revision histories

Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.

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• Listing endpoints and checking their configuration
• Creating an endpoint before model deployment
• Updating endpoint display names or labels

Monitor a data warehouse for stale tables, unusual row counts, column changes, and failed loads. Receive alerts with evidence and likely causes, plus GitHub issues for human review, while warehouse data remains unchanged.

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• Check warehouse health every hour
• Investigate delayed table updates
• Detect unusual row counts or column changes

Create weekly reports from Postgres metrics, comparing current figures with recent weeks. Add plain-language explanations for notable changes, flag missing data, and post one report to Slack while keeping database access read-only.

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• Preparing a weekly metrics report for Slack
• Reviewing weekly changes and recent trends
• Explaining notable changes and flagging data gaps

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

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• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

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• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Collect and update 68 US economic and social indicators, then summarize trends over ten, five, two, and one years. Produce a report covering relationships between indicators, unusual patterns, and topics that warrant further research.

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• Refreshing US economic and social indicators
• Comparing short- and long-term economic trends
• Examining relationships across prices, jobs, and housing

Review AI agent instructions for unnecessary rules, contradictions, repetition, and vague wording. Get recommendations on what to remove, merge, clarify, or load only when needed, plus an estimate of savings in tokens, the units models process.

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• Cleaning up an overloaded CLAUDE.md
• Finding conflicting rules across instruction files
• Checking for redundancy after trimming instructions

Develop varied ideas for names, writing angles, and problem solving by exploring five candidates internally and presenting the strongest. It also expands small example collections into diverse datasets for evaluation, training, or testing.

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• Finding a distinctive product name
• Developing a fresh angle for a blog post
• Exploring solutions to a technical problem

Create and improve prompts for agents, workflows, and AI evaluations. Reusable Handlebars templates, which insert data into a fixed structure, help generate consistent instructions from YAML or JSON inputs for use in other tasks.

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• Improving prompts for recurring tasks
• Generating agent briefs from templates
• Creating AI evaluation and scoring prompts

Apply over 240 reusable prompt templates to summarize content, extract insights, improve writing, review code, or analyze security threats. It reads each template directly and returns results in the format defined for that task.

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• Extract ideas and insights from an article
• Create a concise five-sentence summary
• Improve writing or review code with a selected template

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

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• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Configure OpenTelemetry to send AI application traces, records of execution steps, to Confident AI. Reuse existing exporters, choose the regional endpoint, and add AI-specific attributes without requiring the deepeval Python package.

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• Send existing AI application traces to Confident AI
• Configure tracing for a non-Python AI application
• Choose the US or EU endpoint for an API key

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

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• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Review past dsh agent sessions to identify recurring tasks and test proposed skill or memory improvements. This skill guides preview runs, validation on reserved tasks, staged proposals, adoption with backups, and scheduled nightly cycles.

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• Extract recurring tasks from past agent sessions
• Test proposed skill and memory improvements
• Review staged changes before adopting them

Use past local Codex sessions to propose updates to a selected skill and project memory file. Recurring tasks are replayed, changes are checked on separate validation tasks, and proposals are staged for review before adoption.

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• Turn recurring Codex requests into skill updates
• Consolidate feedback into skills and project memory
• Compare validation scores before applying changes

Review past Claude Code sessions and turn recurring tasks into proposed updates to skills and project memory. Replay tasks, compare scores on separate validation tasks, and review staged edits before applying them with backups.

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• Consolidate repeated Claude Code corrections into reusable rules
• Review proposed changes to SKILL.md and CLAUDE.md
• Try the cycle without provider calls using mock

Review past local Cursor sessions, identify recurring tasks, and propose changes to a project skill. Changes are tested on separate validation tasks and staged for review before adoption, with backups of existing files.

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• Turn recurring Cursor tasks into a reusable skill
• Review proposed edits and validation scores before adoption
• Try the workflow without provider calls using mock

Search LangBot Space for plugins, MCP servers, and skills through MCP, a protocol for connecting agents to tools. Inspect item details, check plugin usage compatibility, and review model availability to support setup decisions.

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• Find LangBot plugins for agent workflows
• Browse MCP servers and skills
• Check model availability during setup

Write, debug, and optimize SQL queries for data analysis and database design. The skill covers aggregations, rankings, missing values, and indexes, using execution plans to understand performance and adapting syntax to the database in use.

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• Writing queries for totals and rankings
• Investigating slow queries with execution plans
• Reviewing table structures and constraints

Helps design semantic search and RAG, where an AI answers using retrieved documents. Covers embeddings, numerical representations of content, plus document splitting, search indexes, keyword search combinations, and evaluation of retrieval quality.

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• Design document retrieval for AI answers
• Compare embedding models and document splitting strategies
• Combine semantic search with keyword search

Adapt large language models to specific tasks using LoRA and QLoRA, methods that train a small set of added parameters. Get guidance on preparing examples, choosing training settings, evaluating outputs, and saving or merging trained adapters.

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• Adapting models to specialized terminology and output formats
• Planning training with limited GPU memory
• Preparing instruction-response datasets

Build probability estimates from historical frequencies and supporting or opposing evidence. Structure forecasts with explicit assumptions and outcome criteria, update them as evidence changes, and assess past predictions using accuracy and confidence calibration.

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• Estimating probabilities for technology or economic developments
• Weighing evidence for and against a forecast
• Updating forecast probabilities when new evidence appears

Build and maintain Elasticsearch search and analytics systems with guidance on queries, field types, and aggregations. Plan index migrations, automate data retention, diagnose cluster allocation problems, and investigate slow searches.

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• Designing full-text search and autocomplete
• Aggregating logs and time-series data
• Planning index migrations and data retention