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.

$npx skills add https://github.com/freestylefly/canghe-skills --skill canghe-image-gen
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summary

Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象), and Canghe providers.

skill.md

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:

  1. SKILL_DIR = this SKILL.md file's directory
  2. 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

  1. --ref provided + no --provider → auto-select Google first, then OpenAI, then Canghe
  2. --provider specified → use it (if --ref, must be google or openai or canghe)
  3. Only one API key available → use that provider
  4. 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 aspectRatio parameter
  • 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

How to use canghe-image-gen on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/freestylefly/canghe-skills --skill canghe-image-gen

The skills CLI fetches canghe-image-gen from GitHub repository freestylefly/canghe-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/canghe-image-gen

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

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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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.540 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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