dbos-python▌
dbos-inc/agent-skills · updated Apr 8, 2026
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Guide for building reliable, fault-tolerant Python applications with DBOS durable workflows.
DBOS Python Best Practices
Guide for building reliable, fault-tolerant Python applications with DBOS durable workflows.
When to Apply
Reference these guidelines when:
- Adding DBOS to existing Python code
- Creating workflows and steps
- Using queues for concurrency control
- Implementing workflow communication (events, messages, streams)
- Configuring and launching DBOS applications
- Using DBOSClient from external applications
- Testing DBOS applications
Rule Categories by Priority
| Priority | Category | Impact | Prefix |
|---|---|---|---|
| 1 | Lifecycle | CRITICAL | lifecycle- |
| 2 | Workflow | CRITICAL | workflow- |
| 3 | Step | HIGH | step- |
| 4 | Queue | HIGH | queue- |
| 5 | Communication | MEDIUM | comm- |
| 6 | Pattern | MEDIUM | pattern- |
| 7 | Testing | LOW-MEDIUM | test- |
| 8 | Client | MEDIUM | client- |
| 9 | Advanced | LOW | advanced- |
Critical Rules
DBOS Configuration and Launch
A DBOS application MUST configure and launch DBOS inside its main function:
import os
from dbos import DBOS, DBOSConfig
@DBOS.workflow()
def my_workflow():
pass
if __name__ == "__main__":
config: DBOSConfig = {
"name": "my-app",
"system_database_url": os.environ.get("DBOS_SYSTEM_DATABASE_URL"),
}
DBOS(config=config)
DBOS.launch()
Workflow and Step Structure
Workflows are comprised of steps. Any function performing complex operations or accessing external services must be a step:
@DBOS.step()
def call_external_api():
return requests.get("https://api.example.com").json()
@DBOS.workflow()
def my_workflow():
result = call_external_api()
return result
Key Constraints
- Do NOT call
DBOS.start_workfloworDBOS.recvfrom a step - Do NOT use threads to start workflows - use
DBOS.start_workflowor queues - Workflows MUST be deterministic - non-deterministic operations go in steps
- Do NOT create/update global variables from workflows or steps
How to Use
Read individual rule files for detailed explanations and examples:
references/lifecycle-config.md
references/workflow-determinism.md
references/queue-concurrency.md
References
How to use dbos-python 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 dbos-python
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dbos-python from GitHub repository dbos-inc/agent-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 dbos-python. Access the skill through slash commands (e.g., /dbos-python) 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▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★43 reviews- ★★★★★William Rao· Dec 24, 2024
dbos-python has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Noor Martin· Dec 20, 2024
We added dbos-python from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 16, 2024
dbos-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kabir Li· Dec 12, 2024
Keeps context tight: dbos-python is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aditi Farah· Dec 8, 2024
dbos-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sophia Martin· Nov 19, 2024
dbos-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Lucas Sethi· Nov 15, 2024
Keeps context tight: dbos-python is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zaid Martinez· Nov 3, 2024
dbos-python has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zaid Smith· Oct 22, 2024
Solid pick for teams standardizing on skills: dbos-python is focused, and the summary matches what you get after install.
- ★★★★★Sophia Sharma· Oct 10, 2024
We added dbos-python from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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