Fetches baoyu-imagine from jimliu/baoyu-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/baoyu-imagine
Restart Cursor to activate baoyu-imagine. Access via /baoyu-imagine 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.
CRITICAL: If not found, complete the full setup (provider + model + quality + save location) using AskUserQuestion BEFORE generating any images. Generation is BLOCKED until EXTEND.md is created.
Path
Location
.baoyu-skills/baoyu-imagine/EXTEND.md
Project directory
$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md
User home
Legacy compatibility: if .baoyu-skills/baoyu-image-gen/EXTEND.md exists and the new path does not, runtime renames it to baoyu-imagine. If both files exist, runtime leaves them unchanged and uses the new path.
Paths in promptFiles, image, and ref are resolved relative to the batch file's directory. jobs is optional (overridden by CLI --jobs). Top-level array format (without jobs wrapper) is also accepted.
Options
Option
Description
--prompt <text>, -p
Prompt text
--promptfiles <files...>
Read prompt from files (concatenated)
--image <path>
Output image path (required in single-image mode)
--batchfile <path>
JSON batch file for multi-image generation
--jobs <count>
Worker count for batch mode (default: auto, max from config, built-in default 10)
Model ID (Google: gemini-3-pro-image-preview; OpenAI: gpt-image-1.5; Azure: deployment name such as gpt-image-1.5 or image-prod; OpenRouter: google/gemini-3.1-flash-image-preview; DashScope: qwen-image-2.0-pro; MiniMax: image-01)
--ar <ratio>
Aspect ratio (e.g., 16:9, 1:1, 4:3)
--size <WxH>
Size (e.g., 1024x1024)
--quality normal|2k
Quality preset (default: 2k)
--imageSize 1K|2K|4K
Image size for Google/OpenRouter (default: from quality)
--ref <files...>
Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate, MiniMax subject-reference, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0
--n <count>
Number of images
--json
JSON output
Environment Variables
Variable
Description
OPENAI_API_KEY
OpenAI API key
AZURE_OPENAI_API_KEY
Azure OpenAI API key
OPENROUTER_API_KEY
OpenRouter API key
GOOGLE_API_KEY
Google API key
DASHSCOPE_
✓
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
