mastra▌
mastra-ai/skills · updated Apr 8, 2026
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Reference guide for building agents and workflows with current Mastra APIs.
- ›Always verify against embedded docs in node_modules/@mastra/*/dist/docs/ (installed version) or remote docs at https://mastra.ai/llms.txt before writing code; training data is outdated
- ›Core building blocks: Agents (autonomous, decision-making), Workflows (structured sequences), Tools (extend capabilities), Memory (maintain context), and RAG (external knowledge)
- ›Requires ES2022 modules in TypeScript config and
Mastra Framework Guide
Build AI applications with Mastra. This skill teaches you how to find current documentation and build agents and workflows.
⚠️ Critical: Do not trust internal knowledge
Everything you know about Mastra is likely outdated or wrong. Never rely on memory. Always verify against current documentation.
Your training data contains obsolete APIs, deprecated patterns, and incorrect usage. Mastra evolves rapidly - APIs change between versions, constructor signatures shift, and patterns get refactored.
Prerequisites
Before writing any Mastra code, check if packages are installed:
ls node_modules/@mastra/
- If packages exist: Use embedded docs first (most reliable)
- If no packages: Install first or use remote docs
Available files
References
| User Question | First Check | How To |
|---|---|---|
| "Create/install Mastra project" | references/create-mastra.md |
Setup guide with CLI and manual steps |
| "How do I use Agent/Workflow/Tool?" | references/embedded-docs.md |
Look up in node_modules/@mastra/*/dist/docs/ |
| "How do I use X?" (no packages) | references/remote-docs.md |
Fetch from https://mastra.ai/llms.txt |
| "I'm getting an error..." | references/common-errors.md |
Common errors and solutions |
| "Upgrade from v0.x to v1.x" | references/migration-guide.md |
Version upgrade workflows |
Scripts
scripts/provider-registry.mjs: Look up current providers and models available in the model router. Always run this before using a model to verify provider keys and model names.
Priority order for writing code
⚠️ Never write code without checking current docs first.
-
Embedded docs first (if packages installed)
Look up current docs in
node_modulesfor a package. Example of looking up "Agent" docs in@mastra/core:grep -r "Agent" node_modules/@mastra/core/dist/docs/references- Why: Matches your EXACT installed version
- Most reliable source of truth
- More information:
references/embedded-docs.md
-
Source code second (if packages installed)
If you can't find what you need in the embedded docs, look directly at the source code. This is more time consuming but can provide insights into implementation details.
# Check what's available cat node_modules/@mastra/core/dist/docs/assets/SOURCE_MAP.json | grep '"Agent"' # Read the actual type definition cat node_modules/@mastra/core/dist/[path-from-source-map]- Why: Ultimate source of truth if docs are missing or unclear
- Use when: Embedded docs don't cover your question
- More information:
references/embedded-docs.md
-
Remote docs third (if packages not installed)
You can fetch the latest docs from the Mastra website:
https://mastra.ai/llms.txt- Why: Latest published docs (may be ahead of installed version)
- Use when: Packages not installed or exploring new features
- More information:
references/remote-docs.md
Core concepts
Agents vs workflows
Agent: Autonomous, makes decisions, uses tools Use for: Open-ended tasks (support, research, analysis)
Workflow: Structured sequence of steps Use for: Defined processes (pipelines, approvals, ETL)
Key components
- Tools: Extend agent capabilities (APIs, databases, external services)
- Memory: Maintain context (message history, working memory, semantic recall, observational memory)
- RAG: Query external knowledge (vector stores, graph relationships)
- Storage: Persist data (Postgres, LibSQL, MongoDB)
Mastra Studio
Studio provides an interactive UI for building, testing, and managing agents, workflows, and tools. It helps with debugging and improving your applications iteratively.
Inside a Mastra project, run:
npm run dev
Then open http://localhost:4111 in your browser to access Mastra Studio.
Critical requirements
TypeScript config
Mastra requires ES2022 modules. CommonJS will fail.
{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"moduleResolution": "bundler"
}
}
Model format
Always use "provider/model-name" when defining models using Mastra's model router.
Use the provider registry script to look up available providers and models:
# List all available providers
node scripts/provider-registry.mjs --list
# List all models for a specific provider (sorted newest first)
node scripts/provider-registry.mjs --provider openai
node scripts/provider-registry.mjs --provider anthropic
When the user asks to use a model or provider, always run the script first to verify the provider key and model name are valid. Do not guess model names from memory as they change frequently.
Example model strings:
"openai/gpt-5.4""anthropic/claude-sonnet-4-5""google/gemini-2.5-pro"
When you see errors
Type errors often mean your knowledge is outdated.
Common signs of outdated knowledge:
Property X does not exist on type YCannot find moduleType mismatcherrors- Constructor parameter errors
What to do:
- Check
references/common-errors.md - Verify current API in embedded docs
- Don't assume the error is a user mistake - it might be your outdated knowledge
Development workflow
Always verify before writing code:
-
Check packages installed
ls node_modules/@mastra/ -
Look up current API
- If installed → Use embedded docs
references/embedded-docs.md - If not → Use remote docs
references/remote-docs.md
- If installed → Use embedded docs
-
Write code based on current docs
-
Test in Studio
npm run dev # http://localhost:4111
Resources
- Setup:
references/create-mastra.md - Embedded docs lookup:
references/embedded-docs.md- Start here if packages are installed - Remote docs lookup:
references/remote-docs.md - Common errors:
references/common-errors.md - Migrations:
references/migration-guide.md
How to use mastra 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 mastra
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mastra from GitHub repository mastra-ai/skills 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 mastra. Access the skill through slash commands (e.g., /mastra) 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- ★★★★★Nikhil Mehta· Dec 20, 2024
mastra reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chaitanya Patil· Dec 12, 2024
We added mastra from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Jain· Nov 11, 2024
Registry listing for mastra matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yusuf Chen· Nov 7, 2024
Useful defaults in mastra — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Nov 3, 2024
mastra fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ren Martinez· Oct 26, 2024
mastra has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Shikha Mishra· Oct 22, 2024
mastra is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★James Jain· Oct 2, 2024
Keeps context tight: mastra is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★James Ghosh· Sep 9, 2024
mastra is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ava Singh· Sep 9, 2024
mastra fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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