Deploy Python APIs using the WorkerEntrypoint class pattern with pywrangler CLI; supports all Cloudflare bindings (D1, KV, R2, Workers AI, Durable Objects, Queues)
Requires async-only code: use httpx or aiohttp for HTTP calls, avoid sync libraries like requests and native C extensions
Python Workflows enable durable multi-step DAG automation with
Fetches cloudflare-python-workers from jezweb/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/cloudflare-python-workers
Restart Cursor to activate cloudflare-python-workers. Access via /cloudflare-python-workers in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
uv run pywrangler dev
# Visit http://localhost:8787
6. Deploy
uv run pywrangler deploy
Migration from Pre-December 2025 Workers
If you created a Python Worker before December 2025, you were limited to built-in packages. With pywrangler (Dec 2025), you can now deploy with external packages.
Old Approach (no longer needed):
# Limited to built-in packages only# Could only use httpx, aiohttp, beautifulsoup4, etc.# Error: "You cannot yet deploy Python Workers that depend on# packages defined in requirements.txt [code: 10021]"
from workers import handler
@handlerasyncdefon_scheduled(event, env, ctx):# Run on cron scheduleprint(f"Cron triggered at {event.scheduledTime}")# Do work...await env.MY_KV.put("last_run",str(event.scheduledTime))
Python Workflows enable durable, multi-step automation with automatic retries and state persistence.
Why Decorator Pattern?
Python Workflows use the @step.do() decorator pattern because Python does not easily support anonymous callbacks (unlike JavaScript/TypeScript which allows inline arrow functions). This is a fundamental language difference, not a limitation of Cloudflare's implementation.
Pyodide captures JavaScript promises (thenables) and proxies them as Python awaitables. This enables Promise.all-equivalent behavior using standard Python async patterns:
›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
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate 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
