canghe-image-gen▌
freestylefly/canghe-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象), and Canghe providers.
Image Generation (AI SDK)
Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象), and Canghe providers.
Script Directory
Agent Execution:
SKILL_DIR= this SKILL.md file's directory- Script path =
${SKILL_DIR}/scripts/main.ts
Preferences (EXTEND.md)
Use Bash to check EXTEND.md existence (priority order):
# Check project-level first
test -f .canghe-skills/canghe-image-gen/EXTEND.md && echo "project"
# Then user-level (cross-platform: $HOME works on macOS/Linux/WSL)
test -f "$HOME/.canghe-skills/canghe-image-gen/EXTEND.md" && echo "user"
┌──────────────────────────────────────────────────┬───────────────────┐ │ Path │ Location │ ├──────────────────────────────────────────────────┼───────────────────┤ │ .canghe-skills/canghe-image-gen/EXTEND.md │ Project directory │ ├──────────────────────────────────────────────────┼───────────────────┤ │ $HOME/.canghe-skills/canghe-image-gen/EXTEND.md │ User home │ └──────────────────────────────────────────────────┴───────────────────┘
┌───────────┬───────────────────────────────────────────────────────────────────────────┐ │ Result │ Action │ ├───────────┼───────────────────────────────────────────────────────────────────────────┤ │ Found │ Read, parse, apply settings │ ├───────────┼───────────────────────────────────────────────────────────────────────────┤ │ Not found │ Use defaults │ └───────────┴───────────────────────────────────────────────────────────────────────────┘
EXTEND.md Supports: Default provider | Default quality | Default aspect ratio | Default image size | Default models
Schema: references/config/preferences-schema.md
Usage
# Basic
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A cat" --image cat.png
# With aspect ratio
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
# High quality
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
# From prompt files
npx -y bun ${SKILL_DIR}/scripts/main.ts --promptfiles system.md content.md --image out.png
# With reference images (Google multimodal or OpenAI edits)
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
# With reference images (explicit provider/model)
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png
# Specific provider
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A cat" --image out.png --provider openai
# DashScope (阿里通义万象)
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
# Canghe third-party gateway
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider canghe
Options
| Option | Description |
|---|---|
--prompt <text>, -p |
Prompt text |
--promptfiles <files...> |
Read prompt from files (concatenated) |
--image <path> |
Output image path (required) |
--provider google|openai|dashscope|canghe |
Force provider (default: google) |
--model <id>, -m |
Model ID (--ref with OpenAI requires GPT Image model, e.g. gpt-image-1.5) |
--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 (default: from quality) |
--ref <files...> |
Reference images. Supported by Google multimodal, OpenAI edits (GPT Image models), and Canghe (image_url). If provider omitted: Google first, then OpenAI, then Canghe |
--n <count> |
Number of images |
--json |
JSON output |
Environment Variables
| Variable | Description |
|---|---|
OPENAI_API_KEY |
OpenAI API key |
GOOGLE_API_KEY |
Google API key |
DASHSCOPE_API_KEY |
DashScope API key (阿里云) |
CANGHE_API_KEY |
Canghe API key |
OPENAI_IMAGE_MODEL |
OpenAI model override |
GOOGLE_IMAGE_MODEL |
Google model override |
DASHSCOPE_IMAGE_MODEL |
DashScope model override (default: z-image-turbo) |
CANGHE_IMAGE_MODEL |
Canghe model override (default: gemini-3-pro-image-preview) |
OPENAI_BASE_URL |
Custom OpenAI endpoint |
GOOGLE_BASE_URL |
Custom Google endpoint |
DASHSCOPE_BASE_URL |
Custom DashScope endpoint |
CANGHE_BASE_URL |
Custom Canghe endpoint (default: https://api.canghe.ai/v1) |
Load Priority: CLI args > EXTEND.md > env vars > <cwd>/.canghe-skills/.env > ~/.canghe-skills/.env
Provider Selection
--refprovided + no--provider→ auto-select Google first, then OpenAI, then Canghe--providerspecified → use it (if--ref, must begoogleoropenaiorcanghe)- Only one API key available → use that provider
- Multiple available → default to Google
Quality Presets
| Preset | Google imageSize | OpenAI Size | Use Case |
|---|---|---|---|
normal |
1K | 1024px | Quick previews |
2k (default) |
2K | 2048px | Covers, illustrations, infographics |
Google imageSize: Can be overridden with --imageSize 1K|2K|4K
Aspect Ratios
Supported: 1:1, 16:9, 9:16, 4:3, 3:4, 2.35:1
- Google multimodal: uses
imageConfig.aspectRatio - Google Imagen: uses
aspectRatioparameter - OpenAI: maps to closest supported size
Generation Mode
Default: Sequential generation (one image at a time). This ensures stable output and easier debugging.
Parallel Generation: Only use when user explicitly requests parallel/concurrent generation.
| Mode | When to Use |
|---|---|
| Sequential (default) | Normal usage, single images, small batches |
| Parallel | User explicitly requests, large batches (10+) |
Parallel Settings (when requested):
| Setting | Value |
|---|---|
| Recommended concurrency | 4 subagents |
| Max concurrency | 8 subagents |
| Use case | Large batch generation when user requests parallel |
Agent Implementation (parallel mode only):
# Launch multiple generations in parallel using Task tool
# Each Task runs as background subagent with run_in_background=true
# Collect results via TaskOutput when all complete
Error Handling
- Missing API key → error with setup instructions
- Generation failure → auto-retry once
- Invalid aspect ratio → warning, proceed with default
- Reference images with unsupported provider/model → error with fix hint (switch to Google multimodal or OpenAI GPT Image edits)
Extension Support
Custom configurations via EXTEND.md. See Preferences section for paths and supported options.
How to use canghe-image-gen 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 canghe-image-gen
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches canghe-image-gen from GitHub repository freestylefly/canghe-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 canghe-image-gen. Access the skill through slash commands (e.g., /canghe-image-gen) 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
4.5★★★★★40 reviews- ★★★★★Maya Li· Dec 12, 2024
Solid pick for teams standardizing on skills: canghe-image-gen is focused, and the summary matches what you get after install.
- ★★★★★Xiao Jain· Dec 12, 2024
We added canghe-image-gen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Shikha Mishra· Dec 8, 2024
Registry listing for canghe-image-gen matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amina Bhatia· Dec 8, 2024
canghe-image-gen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Dec 4, 2024
canghe-image-gen has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 23, 2024
Solid pick for teams standardizing on skills: canghe-image-gen is focused, and the summary matches what you get after install.
- ★★★★★Diya Khan· Nov 15, 2024
Registry listing for canghe-image-gen matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zaid Thomas· Nov 3, 2024
canghe-image-gen has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Amina Robinson· Nov 3, 2024
Keeps context tight: canghe-image-gen is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Fatima Lopez· Oct 22, 2024
Keeps context tight: canghe-image-gen is the kind of skill you can hand to a new teammate without a long onboarding doc.
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