Ai Builder

Skill: AI Builder

When to Use

Trigger when building, configuring, or integrating AI Builder models -- document processing, prediction, GPT prompts, prebuilt models, or custom ML models within Power Platform.

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Model Types

| Model Type | Description | Training Required? | |---|---|---| | Document processing | Extract fields from invoices, receipts, custom documents | Yes -- train with sample documents | | Text recognition (OCR) | Extract text from images and PDFs | No (prebuilt) | | Sentiment analysis | Detect positive/negative/neutral sentiment in text | No (prebuilt) | | Entity extraction | Extract entities (names, dates, addresses) from text | No (prebuilt) | | Text classification | Categorize text into custom categories | Yes -- train with labeled examples | | Object detection | Detect and count objects in images | Yes -- train with labeled images | | Prediction | Predict outcomes based on historical data | Yes -- requires Dataverse table with history | | GPT prompts | Custom prompts using Azure OpenAI GPT models | No -- prompt engineering only | | Category classification | Classify items into predefined categories | Yes |

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⚠️ REQUIRED: Load Sub-Files Before Implementation

SKILL.md is a summary only — it is NOT sufficient for implementation.

The detailed content (complete payloads, XML templates, working examples, edge-case handling) lives in sub-files in the same directory as this SKILL.md. Before writing any code, you MUST use read_file on the sub-files relevant to your task:

• [Document Processing](document-processing.md) -- Document processing models, training, publishing, Power Automate integration, batch processing, confidence thresholds, human review loop
• [GPT Prompts](gpt-prompts.md) -- GPT prompt actions, prompt engineering for Power Platform, Power Automate and Canvas App integration, token management
• [Predictions](predictions.md) -- Prediction models, category classification, object detection, training data requirements, scheduling retraining, performance monitoring

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Licensing

| Capability | License Requirement | |---|---| | Prebuilt models (OCR, sentiment) | Included with Power Apps/Automate Premium | | Custom models (document processing, prediction) | AI Builder add-on capacity (credits) | | GPT prompts | AI Builder add-on capacity (credits) | | Consumption | Credit-based -- different models consume different credits per call |

> Tip: Start with prebuilt models (no additional cost with Premium license) before investing in custom model training.

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Anti-Patterns

• Training document processing models with fewer than 5 sample documents (poor accuracy)
• Not testing models with documents that differ from training samples
• Using GPT prompts for deterministic logic (use expressions or business rules instead)
• Hardcoding model IDs in flows -- use environment variables for environment promotion
• Not monitoring AI Builder credit consumption (can exhaust capacity unexpectedly)
• Processing sensitive data through GPT prompts without reviewing data residency policies
• Using custom models when a prebuilt model already handles the document type
• No error handling around AI Builder actions in flows (models can fail on unexpected input)

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Related Skills

• power-automate -- AI Builder actions are most commonly used in cloud flows
• canvas-apps -- AI Builder components for in-app document processing and predictions
• dataverse -- Prediction models train on Dataverse table data; results stored in Dataverse
• alm -- AI Builder models are solution-aware and promote with managed solutions
• copilot-studio -- GPT prompts can be used as plugin actions in Copilot agents
• azure-openai -- When AI Builder GPT limits are insufficient, escalate to direct Azure OpenAI

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: korchard333
• Source: korchard333/claude-power-platform-community
• License: MIT

Install and usage instructions live in the source repository linked above.