数据与 AI技能

Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.

中文简介正在准备中。当前显示英语简介。

• Choosing an execution loop for a tool-using agent
• Designing memory and recovery for long-running tasks
• Setting tool permissions, runtime limits, and cost caps

Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.

中文简介正在准备中。当前显示英语简介。

• Finding bottlenecks in workflows involving multiple agents
• Reviewing task distribution and parallel execution
• Reducing model costs within quality thresholds

Plan AI features for products, covering language model integration, RAG architecture that retrieves information for answers, prompt design, user experiences people can trust, and cost optimization. Use it to guide development decisions for production use.

中文简介正在准备中。当前显示英语简介。

• Plan language model integration in a product
• Consider RAG architecture for reference-based answers
• Review prompt design and AI user experience

Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.

中文简介正在准备中。当前显示英语简介。

• Plan AI features and language model integration
• Organize RAG retrieval design and implementation
• Map agent roles, tools, and memory

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

中文简介正在准备中。当前显示英语简介。

• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Create a dedicated AI subagent for recurring work, with a defined role, working process, output format, and constraints. Package its files in a plugin and optionally draft a companion skill that routes suitable tasks to it.

中文简介正在准备中。当前显示英语简介。

• Creating a dedicated code review agent
• Turning repeated instructions into an agent persona
• Adding related agents to an existing plugin

Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.

中文简介正在准备中。当前显示英语简介。

• Save architecture decisions for later reference
• Search previously recorded implementation patterns
• Retrieve design notes by key

Design agent memory that preserves facts, preferences, and conversation history across sessions. Plan storage, retrieval, and retention separately for each organization or user, with checks for unauthorized access, outdated information, conflicting records, and retrieval quality.

中文简介正在准备中。当前显示英语简介。

• Designing memory that remembers preferences across sessions
• Separating conversation history by organization and user
• Investigating retrieval of outdated or conflicting records

Build persistent agents on Azure AI Foundry with the Microsoft Agent Framework Python SDK. Connect functions, search, and MCP tools, preserve conversation context, stream responses, and return data in a defined structure.

中文简介正在准备中。当前显示英语简介。

• Building an agent that preserves conversation context
• Connecting Python functions and hosted search tools
• Displaying responses as they arrive

Store project knowledge and design decisions in persistent memory that AI agents can search and read. New findings sync automatically to Markdown documentation, while usage statistics help you inspect the memory bank.

中文简介正在准备中。当前显示英语简介。

• Save architecture decisions for later tasks
• Find previously recorded implementation patterns
• Keep agent memory synced with project documentation

Design criteria for using an LLM to judge AI responses through scoring or comparisons between two answers. Includes checks for order and length bias, plus validation against human judgments to assess evaluation reliability.

中文简介正在准备中。当前显示英语简介。

• Build an automated evaluation pipeline for AI responses
• Compare responses after changing a prompt or model
• Define shared scoring criteria for reviewers

Route a goal through acc's scored-memory workflow, which uses evaluated stored information. Handle returned reasoning frames, submit proposals with confidence estimates, and present final answers with reference IDs before recording the eventual outcome.

中文简介正在准备中。当前显示英语简介。

• Submit a goal to acc's scored-memory workflow
• Review a returned frame and submit a proposal
• Present answers with commitment and reference IDs

Plan an A/B test to compare product changes, with a clear hypothesis, variants, sample estimates, and tracking checks. Define success metrics and stopping rules before launch, then document results and decisions.

中文简介正在准备中。当前显示英语简介。

• Planning a signup form A/B test
• Estimating sample size and test duration
• Checking event tracking before launch

Improve an existing AI agent by analyzing execution records, user feedback, and performance metrics. Refine prompts and workflows, compare original and revised versions through tests, and plan staged releases with monitoring and rollback when results worsen.

中文简介正在准备中。当前显示英语简介。

• Investigating recurring agent failures and user corrections
• Refining prompts, examples, and output formats
• Comparing original and revised agents with A/B tests

Write and review SQL for reports and metrics in a data warehouse, a store of analytical data. Applies rules for explicit columns, date filters, aggregate names, and limited exploratory results, returning SQL with a brief explanation.

中文简介正在准备中。当前显示英语简介。

• Write SQL for report and dashboard metrics
• Calculate counts, sums, and rolling metrics
• Combine data from multiple tables

Explore World Cup match predictions, related news, and market probabilities. Adjust model factors to compare possible outcomes, then optionally hand off to Binance Agentic Wallet for prediction-market trading, where orders require an explicit choice and confirmation.

中文简介正在准备中。当前显示英语简介。

• Review AI predictions for a World Cup match
• Compare model probabilities with market probabilities
• Recalculate probabilities with adjusted factors

Find ranked crypto tokens and trader addresses by social buzz, search popularity, capital inflows, or trading profit and loss. Compare results across supported blockchains, with time windows and filters available for relevant rankings.

中文简介正在准备中。当前显示英语简介。

• Find tokens attracting social buzz
• Compare tokens by net capital inflows
• Filter trending tokens by market size

Prepare an evaluation framework before fine-tuning a language model. Build labeled examples, checks for each failure type, and a base-model benchmark, while calibrating model-based judges against human labels to support comparisons after training.

中文简介正在准备中。当前显示英语简介。

• Build an evaluation set from agent traces
• Create separate checks for each failure type
• Calibrate an LLM judge against human labels

Design backtests that evaluate trading strategies against historical data. Account for trading costs, avoid using future information or excluding delisted securities, and compare strategies using separate development and evaluation periods.

中文简介正在准备中。当前显示英语简介。

• Build a backtesting system for trading strategies
• Check for bias in historical performance estimates
• Compare strategies after accounting for trading costs

Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.

中文简介正在准备中。当前显示英语简介。

• Configure training for an image-and-text task
• Decide whether to train vision layers
• Investigate training with flat evaluation scores

Calculate portfolio risk and performance measures, including potential losses, declines from past peaks, and returns relative to risk. Use multiple indicators to support risk limits, position sizing, and ongoing monitoring.

中文简介正在准备中。当前显示英语简介。

• Measuring portfolio loss risk
• Comparing returns relative to risk
• Setting risk limits and position sizes

Prepare and review LoRA or QLoRA settings for adapting language models using example responses. Choose trainable layers, adapter capacity, learning rate, and memory-related options, then produce configuration values for a downstream training script.

中文简介正在准备中。当前显示英语简介。

• Preparing a LoRA configuration for supervised fine-tuning
• Reviewing rank, alpha, and learning rate settings
• Choosing LoRA or QLoRA based on GPU memory

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

中文简介正在准备中。当前显示英语简介。

• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Choose a model training method and configuration from paired response preferences or individual positive and negative ratings. Covers DPO, ORPO, KTO, and SimPO, including pair construction, repeated training rounds, and troubleshooting learning settings.

中文简介正在准备中。当前显示英语简介。

• Choose a training method for paired response preferences
• Select a method for individual positive and negative ratings
• Review learning settings when DPO training loses general capability