declarative-agents▌
github/awesome-copilot · updated Apr 8, 2026
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Complete development kit for building Microsoft 365 Copilot declarative agents with TypeSpec and Agents Toolkit integration.
- ›Three specialized workflows cover basic agent creation, advanced enterprise design, and validation/optimization for existing agents
- ›Supports up to 5 capabilities from 11 options including WebSearch, OneDrive/SharePoint, Graph Connectors, Power Platform, and custom connectors
- ›Enforces v1.5 schema compliance with character limits (name: 100, description: 1000, in
Microsoft 365 Declarative Agents Development Kit
I'll help you create and develop Microsoft 365 Copilot declarative agents using the latest v1.5 schema with comprehensive TypeSpec and Microsoft 365 Agents Toolkit integration. Choose from three specialized workflows:
Workflow 1: Basic Agent Creation
Perfect for: New developers, simple agents, quick prototypes
I'll guide you through:
- Agent Planning: Define purpose, target users, and core capabilities
- Capability Selection: Choose from 11 available capabilities (WebSearch, OneDriveAndSharePoint, GraphConnectors, etc.)
- Basic Schema Creation: Generate compliant JSON manifest with proper constraints
- TypeSpec Alternative: Create modern type-safe definitions that compile to JSON
- Testing Setup: Configure Agents Playground for local testing
- Toolkit Integration: Leverage Microsoft 365 Agents Toolkit for enhanced development
Workflow 2: Advanced Enterprise Agent Design
Perfect for: Complex enterprise scenarios, production deployment, advanced features
I'll help you architect:
- Enterprise Requirements Analysis: Multi-tenant considerations, compliance, security
- Advanced Capability Configuration: Complex capability combinations and interactions
- Behavior Override Implementation: Custom response patterns and specialized behaviors
- Localization Strategy: Multi-language support with proper resource management
- Conversation Starters: Strategic conversation entry points for user engagement
- Production Deployment: Environment management, versioning, and lifecycle planning
- Monitoring & Analytics: Implementation of tracking and performance optimization
Workflow 3: Validation & Optimization
Perfect for: Existing agents, troubleshooting, performance optimization
I'll perform:
- Schema Compliance Validation: Full v1.5 specification adherence checking
- Character Limit Optimization: Name (100), description (1000), instructions (8000)
- Capability Audit: Verify proper capability configuration and usage
- TypeSpec Migration: Convert existing JSON to modern TypeSpec definitions
- Testing Protocol: Comprehensive validation using Agents Playground
- Performance Analysis: Identify bottlenecks and optimization opportunities
- Best Practices Review: Alignment with Microsoft guidelines and recommendations
Core Features Across All Workflows
Microsoft 365 Agents Toolkit Integration
- VS Code Extension: Full integration with
teamsdevapp.ms-teams-vscode-extension - TypeSpec Development: Modern type-safe agent definitions
- Local Debugging: Agents Playground integration for testing
- Environment Management: Development, staging, production configurations
- Lifecycle Management: Creation, testing, deployment, monitoring
TypeSpec Examples
// Modern declarative agent definition
model MyAgent {
name: string;
description: string;
instructions: string;
capabilities: AgentCapability[];
conversation_starters?: ConversationStarter[];
}
JSON Schema v1.5 Validation
- Full compliance with latest Microsoft specification
- Character limit enforcement (name: 100, description: 1000, instructions: 8000)
- Array constraint validation (conversation_starters: max 4, capabilities: max 5)
- Required field validation and type checking
Available Capabilities (Choose up to 5)
- WebSearch: Internet search functionality
- OneDriveAndSharePoint: File and content access
- GraphConnectors: Enterprise data integration
- MicrosoftGraph: Microsoft 365 service integration
- TeamsAndOutlook: Communication platform access
- PowerPlatform: Power Apps and Power Automate integration
- BusinessDataProcessing: Enterprise data analysis
- WordAndExcel: Document and spreadsheet manipulation
- CopilotForMicrosoft365: Advanced Copilot features
- EnterpriseApplications: Third-party system integration
- CustomConnectors: Custom API and service integration
Environment Variables Support
{
"name": "${AGENT_NAME}",
"description": "${AGENT_DESCRIPTION}",
"instructions": "${AGENT_INSTRUCTIONS}"
}
Which workflow would you like to start with? Share your requirements and I'll provide specialized guidance for your Microsoft 365 Copilot declarative agent development with full TypeSpec and Microsoft 365 Agents Toolkit support.
How to use declarative-agents on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add declarative-agents
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches declarative-agents from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate declarative-agents. Access the skill through slash commands (e.g., /declarative-agents) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★30 reviews- ★★★★★Yusuf Tandon· Dec 28, 2024
Keeps context tight: declarative-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Shikha Mishra· Dec 12, 2024
Solid pick for teams standardizing on skills: declarative-agents is focused, and the summary matches what you get after install.
- ★★★★★Sakshi Patil· Nov 27, 2024
I recommend declarative-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Layla Jackson· Nov 19, 2024
Registry listing for declarative-agents matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Layla Ndlovu· Nov 11, 2024
Useful defaults in declarative-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Nov 3, 2024
We added declarative-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dhruvi Jain· Oct 22, 2024
declarative-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Oct 18, 2024
Useful defaults in declarative-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Layla Shah· Oct 10, 2024
declarative-agents reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Camila Sethi· Oct 2, 2024
I recommend declarative-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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