Fetches deepseek from vm0-ai/vm0-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/deepseek
Restart Cursor to activate deepseek. Access via /deepseek 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.
Top up your balance (no free tier, but very affordable pricing)
exportDEEPSEEK_API_KEY="your-api-key"
Pricing (per 1M tokens)
Type
Price
Input (cache hit)
$0.028
Input (cache miss)
$0.28
Output
$0.42
Rate Limits
DeepSeek does not enforce strict rate limits. They will try to serve every request. During high traffic, connections are maintained with keep-alive signals.
How to Use
All examples below assume you have DEEPSEEK_API_KEY set.
The base URL for the DeepSeek API is:
https://api.deepseek.com (recommended)
https://api.deepseek.com/v1 (OpenAI-compatible)
1. Basic Chat Completion
Send a simple chat message:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-chat","messages":[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Hello, who are you?"}]}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json
Available models:
deepseek-chat: DeepSeek-V3.2 non-thinking mode (128K context, 8K max output)
deepseek-reasoner: DeepSeek-V3.2 thinking mode (128K context, 64K max output)
2. Chat with Temperature Control
Adjust creativity/randomness with temperature:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-chat","messages":[{"role":"user","content":"Write a short poem about coding."}],"temperature":0.7,"max_tokens":200}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json | jq -r'.choices[0].message.content'
Parameters:
temperature (0-2, default 1): Higher = more creative, lower = more deterministic
{"model":"deepseek-chat","messages":[{"role":"user","content":"Explain quantum computing in simple terms."}],"stream":true}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json
Streaming returns Server-Sent Events (SSE) with delta chunks, ending with data: [DONE].
4. Deep Reasoning (Thinking Mode)
Use the reasoner model for complex reasoning tasks:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-reasoner","messages":[{"role":"user","content":"What is 15 * 17? Show your work."}]}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json | jq -r'.choices[0].message.content'
The reasoner model excels at math, logic, and multi-step problems.
5. JSON Output Mode
Force the model to return valid JSON:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-chat","messages":[{"role":"system","content":"You are a JSON generator. Always respond with valid JSON."},{"role":"user","content":"List 3 programming languages with their main use cases."}],"response_format":{"type":"json_object"}}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json | jq -r'.choices[0].message.content'
6. Multi-turn Conversation
Continue a conversation with message history:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-chat","messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Nice to meet you, Alice."},{"role":"user","content":"What is my name?"}]}
Then run:
curl-s"https://api.deepseek.com/chat/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json | jq -r'.choices[0].message.content'
7. Code Completion (FIM)
Use Fill-in-the-Middle for code completion (beta endpoint):
curl-s"https://api.deepseek.com/beta/completions"-X POST -H"Content-Type: application/json"-H"Authorization: Bearer $DEEPSEEK_API_KEY"-d @/tmp/deepseek_request.json | jq -r'.choices[0].text'
FIM is useful for:
Code completion in editors
Filling gaps in documents
Context-aware text generation
8. Function Calling (Tools)
Define functions the model can call:
Write to /tmp/deepseek_request.json:
{"model":"deepseek-chat","messages":[{"role":"user","content":"What is the weather in Tokyo?"}],"tools":[{"type":"function","function":{"name":"get_weather","description":"Get the current weather for a location","parameters":{"type":"object","properties":
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›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
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share 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
