Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象), and Canghe providers.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncanghe-image-genExecute the skills CLI command in your project's root directory to begin installation:
Fetches canghe-image-gen from freestylefly/canghe-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate canghe-image-gen. Access via /canghe-image-gen in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象), and Canghe providers.
Agent Execution:
SKILL_DIR = this SKILL.md file's directory${SKILL_DIR}/scripts/main.tsUse 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
# 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
| 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 |
| 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
--ref provided + no --provider → auto-select Google first, then OpenAI, then Canghe--provider specified → use it (if --ref, must be google or openai or canghe)| 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
Supported: 1:1, 16:9, 9:16, 4:3, 3:4, 2.35:1
imageConfig.aspectRatioaspectRatio parameterDefault: 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
Custom configurations via EXTEND.md. See Preferences section for paths and supported options.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
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cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: canghe-image-gen is focused, and the summary matches what you get after install.
We added canghe-image-gen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for canghe-image-gen matched our evaluation — installs cleanly and behaves as described in the markdown.
canghe-image-gen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
canghe-image-gen has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: canghe-image-gen is focused, and the summary matches what you get after install.
Registry listing for canghe-image-gen matched our evaluation — installs cleanly and behaves as described in the markdown.
canghe-image-gen has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: canghe-image-gen is the kind of skill you can hand to a new teammate without a long onboarding doc.
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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