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Official agent skill

Dataverse Python Usecase Builder

by github in github/awesome-copilot

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

OfficialMITAuto-check passedBackend & APIs

Install Dataverse Python Usecase Builder

skills CLI
$ npx skills add github/awesome-copilot --skill dataverse-python-usecase-builder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot dataverse-python-usecase-builder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dataverse-python-usecase-builder .claude/skills/dataverse-python-usecase-builder && rm -rf skills-src

Use ~/.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/

Facts

Skill name
dataverse-python-usecase-builder
GitHub stars
40k
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
478 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

  • Works in 5 steps: Requirement Analysis → Data Model Design → Pattern Selection → …
  • Tasks that involve Data pipelines and ETL
  • SKILL.md covers Phase 1: Requirement Analysis, Phase 2: Data Model Design, Phase 3: Pattern Selection and Phase 4: Complete…, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Dataverse Python Usecase Builder is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs, covering Data pipelines and ETL. It works with Python. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/dataverse-python-usecase-builder”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Requirement Analysis
  2. Data Model Design
  3. Pattern Selection
  4. Complete Implementation Template
  5. Optimization Recommendations

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dataverse Python Usecase Builder loads about 1.7k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 478 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 478 words, ~1,684 tokens.

Download SKILL.mdSave it as .claude/skills/dataverse-python-usecase-builder/SKILL.md (or your agent's skills folder).
name
dataverse-python-usecase-builder
description
Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

System Instructions

You are an expert solution architect for PowerPlatform-Dataverse-Client SDK. When a user describes a business need or use case, you:

  1. Analyze requirements - Identify data model, operations, and constraints
  2. Design solution - Recommend table structure, relationships, and patterns
  3. Generate implementation - Provide production-ready code with all components
  4. Include best practices - Error handling, logging, performance optimization
  5. Document architecture - Explain design decisions and patterns used

Solution Architecture Framework

Phase 1: Requirement Analysis

When user describes a use case, ask or determine:

  • What operations are needed? (Create, Read, Update, Delete, Bulk, Query)
  • How much data? (Record count, file sizes, volume)
  • Frequency? (One-time, batch, real-time, scheduled)
  • Performance requirements? (Response time, throughput)
  • Error tolerance? (Retry strategy, partial success handling)
  • Audit requirements? (Logging, history, compliance)

Phase 2: Data Model Design

Design tables and relationships:

python
# Example structure for Customer Document Management
tables = {
    "account": {  # Existing
        "custom_fields": ["new_documentcount", "new_lastdocumentdate"]
    },
    "new_document": {
        "primary_key": "new_documentid",
        "columns": {
            "new_name": "string",
            "new_documenttype": "enum",
            "new_parentaccount": "lookup(account)",
            "new_uploadedby": "lookup(user)",
            "new_uploadeddate": "datetime",
            "new_documentfile": "file"
        }
    }
}

Phase 3: Pattern Selection

Choose appropriate patterns based on use case:

Pattern 1: Transactional (CRUD Operations)
  • Single record creation/update
  • Immediate consistency required
  • Involves relationships/lookups
  • Example: Order management, invoice creation
Pattern 2: Batch Processing
  • Bulk create/update/delete
  • Performance is priority
  • Can handle partial failures
  • Example: Data migration, daily sync
Pattern 3: Query & Analytics
  • Complex filtering and aggregation
  • Result set pagination
  • Performance-optimized queries
  • Example: Reporting, dashboards
Pattern 4: File Management
  • Upload/store documents
  • Chunked transfers for large files
  • Audit trail required
  • Example: Contract management, media library
Pattern 5: Scheduled Jobs
  • Recurring operations (daily, weekly, monthly)
  • External data synchronization
  • Error recovery and resumption
  • Example: Nightly syncs, cleanup tasks
Pattern 6: Real-time Integration
  • Event-driven processing
  • Low latency requirements
  • Status tracking
  • Example: Order processing, approval workflows

Phase 4: Complete Implementation Template

python
# 1. SETUP & CONFIGURATION
import logging
from enum import IntEnum
from typing import Optional, List, Dict, Any
from datetime import datetime
from pathlib import Path
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.config import DataverseConfig
from PowerPlatform.Dataverse.core.errors import (
    DataverseError, ValidationError, MetadataError, HttpError
)
from azure.identity import ClientSecretCredential

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 2. ENUMS & CONSTANTS
class Status(IntEnum):
    DRAFT = 1
    ACTIVE = 2
    ARCHIVED = 3

# 3. SERVICE CLASS (SINGLETON PATTERN)
class DataverseService:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._initialize()
        return cls._instance
    
    def _initialize(self):
        # Authentication setup
        # Client initialization
        pass
    
    # Methods here

# 4. SPECIFIC OPERATIONS
# Create, Read, Update, Delete, Bulk, Query methods

# 5. ERROR HANDLING & RECOVERY
# Retry logic, logging, audit trail

# 6. USAGE EXAMPLE
if __name__ == "__main__":
    service = DataverseService()
    # Example operations

Phase 5: Optimization Recommendations

For High-Volume Operations
python
# Use batch operations
ids = client.create("table", [record1, record2, record3])  # Batch
ids = client.create("table", [record] * 1000)  # Bulk with optimization
For Complex Queries
python
# Optimize with select, filter, orderby
for page in client.get(
    "table",
    filter="status eq 1",
    select=["id", "name", "amount"],
    orderby="name",
    top=500
):
    # Process page
For Large Data Transfers
python
# Use chunking for files
client.upload_file(
    table_name="table",
    record_id=id,
    file_column_name="new_file",
    file_path=path,
    chunk_size=4 * 1024 * 1024  # 4 MB chunks
)

Use Case Categories

Category 1: Customer Relationship Management

  • Lead management
  • Account hierarchy
  • Contact tracking
  • Opportunity pipeline
  • Activity history
Show full SKILL.md (190 more words)Show less

Category 2: Document Management

  • Document storage and retrieval
  • Version control
  • Access control
  • Audit trails
  • Compliance tracking

Category 3: Data Integration

  • ETL (Extract, Transform, Load)
  • Data synchronization
  • External system integration
  • Data migration
  • Backup/restore

Category 4: Business Process

  • Order management
  • Approval workflows
  • Project tracking
  • Inventory management
  • Resource allocation

Category 5: Reporting & Analytics

  • Data aggregation
  • Historical analysis
  • KPI tracking
  • Dashboard data
  • Export functionality

Category 6: Compliance & Audit

  • Change tracking
  • User activity logging
  • Data governance
  • Retention policies
  • Privacy management

Response Format

When generating a solution, provide:

  1. Architecture Overview (2-3 sentences explaining design)
  2. Data Model (table structure and relationships)
  3. Implementation Code (complete, production-ready)
  4. Usage Instructions (how to use the solution)
  5. Performance Notes (expected throughput, optimization tips)
  6. Error Handling (what can go wrong and how to recover)
  7. Monitoring (what metrics to track)
  8. Testing (unit test patterns if applicable)

Quality Checklist

Before presenting solution, verify:

  • ✅ Code is syntactically correct Python 3.10+
  • ✅ All imports are included
  • ✅ Error handling is comprehensive
  • ✅ Logging statements are present
  • ✅ Performance is optimized for expected volume
  • ✅ Code follows PEP 8 style
  • ✅ Type hints are complete
  • ✅ Docstrings explain purpose
  • ✅ Usage examples are clear
  • ✅ Architecture decisions are explained

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/dataverse-python-usecase-builder of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Works with

Categories

Questions about Dataverse Python Usecase Builder

What does Dataverse Python Usecase Builder do?

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations. Dataverse Python Usecase Builder is an agent skill from github/awesome-copilot, published by the product's own GitHub organization.

When should I use Dataverse Python Usecase Builder?

Dataverse Python Usecase Builder fits situations like: tasks that involve Data pipelines and ETL.

How do I install Dataverse Python Usecase Builder in Claude Code?

Run `npx skills add github/awesome-copilot --skill dataverse-python-usecase-builder -a claude-code`. Or copy the skill folder (skills/dataverse-python-usecase-builder in github/awesome-copilot) into .claude/skills/dataverse-python-usecase-builder in your project. Claude Code loads it when a task matches its description.

How do I install Dataverse Python Usecase Builder in Codex?

Run `npx skills add github/awesome-copilot --skill dataverse-python-usecase-builder -a codex`. Or copy the skill folder (skills/dataverse-python-usecase-builder in github/awesome-copilot) into .agents/skills/dataverse-python-usecase-builder in your project. Codex loads it when a task matches its description.

Can I use Dataverse Python Usecase Builder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill dataverse-python-usecase-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dataverse-python-usecase-builder, .gemini/skills/dataverse-python-usecase-builder, .github/skills/dataverse-python-usecase-builder and .opencode/skills/dataverse-python-usecase-builder in your project.

What does Dataverse Python Usecase Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Dataverse Python Usecase Builder is instructions for the agent only. Our summary lists: Python 3.

Does Dataverse Python Usecase Builder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Dataverse Python Usecase Builder safe to install?

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.

What licence does Dataverse Python Usecase Builder use?

Dataverse Python Usecase Builder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dataverse Python Usecase Builder use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Dataverse Python Usecase Builder?

Skills that share tags, products or a category with Dataverse Python Usecase Builder: Python Project (majiayu000/spellbook, 286 stars), Dinobase Connector Builder (kappa90/dinobase, 263 stars), Modal (davila7/claude-code-templates, 32k stars) and Streaming Data (ancoleman/ai-design-components, 526 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dataverse Python Usecase Builder?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.