Chapter Image Enhancer logo

Chapter Image Enhancer

Community
dmccreary
chapter-image-enhancer

Adds freely-licensed maps and photos to textbook chapters by sourcing from Wikimedia Commons and US government archives, then inserting them with captions and proper attribution.

Overview

Publisherdmccreary
Repositoryibook-skills
Skill namechapter-image-enhancer
Stars
109
Forks
21
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dmccreary on GitHub. Read the source before you install it.

Installation

Install the Chapter Image Enhancer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dmccreary/ibook-skills/tree/main/skills/archived/chapter-image-enhancer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dmccreary/ibook-skills.git /tmp/ibook-skills
mkdir -p .claude/skills
cp -r /tmp/ibook-skills/skills/archived/chapter-image-enhancer .claude/skills/chapter-image-enhancer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chapter Image Enhancer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chapter Image Enhancer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chapter Image Enhancer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Chapter Image Enhancer

Add high-quality, freely-licensed images to intelligent textbook chapters. This skill finds relevant maps and photos from public domain and Creative Commons sources, downloads and optimizes them for web, inserts them at appropriate locations with captions, and adds proper attribution.

When to Use

  • A chapter is text-heavy and would benefit from maps, photos, or diagrams
  • The user asks to "add images", "make it more visual", or "find photos for" a chapter
  • A chapter covers geographic, scientific, or visual topics where images are essential
  • After chapter content is generated but before final publication

Image Source Priority

The skill uses only images compatible with CC BY-NC-SA 4.0 textbooks. Search in this order:

Tier 1: US Government (Public Domain — No Restrictions)

US federal government works are public domain and the safest to use:

  • USDA — hardiness zone maps, soil maps, agricultural photos
  • EPA — ecoregion maps, environmental data visualizations
  • USGS — topographic maps, geological surveys, satellite imagery
  • USFWS — wildlife and habitat photos (Fish & Wildlife Service)
  • US Forest Service — forest and wilderness photos
  • NOAA — weather, climate, and ocean imagery
  • NASA — Earth observation and satellite photos
  • NPS — National Park Service landscape photos
  • Library of Congress — historical photos and maps

Tier 2: Wikimedia Commons (CC BY or CC BY-SA)

Wikimedia Commons hosts millions of freely-licensed educational images:

  • CC BY 2.0/3.0/4.0 — attribution required, fully compatible
  • CC BY-SA 2.0/3.0/4.0 — attribution + share-alike, compatible with CC BY-NC-SA
  • CC0 / Public Domain — no restrictions

Avoid these licenses (incompatible with CC BY-NC-SA 4.0):

  • CC BY-ND — No Derivatives conflicts with SA
  • All Rights Reserved — cannot use without explicit permission
  • CC BY-NC-ND — No Derivatives is incompatible

Tier 3: State Government and University Sources

State works are NOT automatically public domain (unlike federal). Check terms of use individually. Many state agencies and universities grant educational use.

Workflow

Step 1: Analyze the Chapter

Read the chapter's index.md to understand:

  • The chapter title and main topic
  • Major H2 sections — each is a candidate for an image
  • What types of images would be most helpful (maps, photos, diagrams, charts)
  • The subject domain (geography, biology, history, etc.) to guide image search

Identify 3-6 insertion points where an image would add the most value. Prioritize:

  1. Geographic topics → maps
  2. Species or organisms → photos
  3. Processes or systems → diagrams
  4. Historical topics → archival photos
  5. Locations or landmarks → landscape photos

Step 2: Find Images

For each insertion point, find a suitable image.

Using the Wikimedia API to get correct download URLs:

The Wikimedia API reliably returns actual image URLs (direct curl/wget often fails due to hash-path issues):

python
import urllib.request, json

title = "File:Example_Image.jpg"
api_url = f"https://en.wikipedia.org/w/api.php?action=query&titles={urllib.request.quote(title)}&prop=imageinfo&iiprop=url&iiurlwidth=1280&format=json"
req = urllib.request.Request(api_url, headers={"User-Agent": "TextbookProject/1.0 (Educational; CC-BY-NC-SA)"})
with urllib.request.urlopen(req) as resp:
    data = json.loads(resp.read())
    pages = data["query"]["pages"]
    for pid, page in pages.items():
        if "imageinfo" in page:
            url = page["imageinfo"][0].get("thumburl", page["imageinfo"][0]["url"])

The iiurlwidth=1280 parameter requests a pre-scaled thumbnail — this is faster than downloading the full-resolution original and resizing locally.

For US Government sources, direct URLs typically work fine with curl.

Important: Always verify the license before downloading. On Wikimedia Commons, check the file page for the license template. For US government images, confirm they come from a federal (not state or contractor) source.

Step 3: Download Images

Create a directory for the chapter's images:

docs/img/chapters/XX-chapter-name/

Download with Python's urllib (more reliable than curl for Wikimedia):

python
import urllib.request

headers = {"User-Agent": "TextbookProject/1.0 (Educational)"}
req = urllib.request.Request(url, headers=headers)
with urllib.request.urlopen(req) as resp:
    data = resp.read()
    with open(filepath, "wb") as f:
        f.write(data)

Note: Wikimedia rate-limits aggressively. If you get HTTP 429, wait 5 seconds and retry. Download images sequentially, not in parallel.

Step 4: Optimize for Web

Resize and compress images using Pillow:

python
from PIL import Image

img = Image.open(path)
max_width = 1200

if img.width > max_width:
    ratio = max_width / img.width
    img = img.resize((max_width, int(img.height * ratio)), Image.LANCZOS)

if path.endswith('.png') and img.mode != 'RGBA':
    # Convert to JPEG for smaller file size (unless transparency needed)
    img.convert('RGB').save(path.replace('.png', '.jpg'), 'JPEG', quality=82, optimize=True)
else:
    img.save(path, optimize=True)

Target file sizes:

  • Photos: under 500KB (JPEG quality 80-85)
  • Maps: under 600KB (JPEG if no transparency, PNG if needed)
  • Diagrams: under 300KB (PNG for sharp lines)

Step 5: Insert Images into the Chapter

Place each image at the identified insertion point using standard Markdown image syntax. Include a caption in italics below the image:

markdown
![Descriptive alt text for accessibility](../../img/chapters/XX-name/filename.jpg)
*Caption describing the image content and context. Source: Creator Name (License).*

Placement rules:

  • Insert images immediately after the section heading (H2) they relate to, before the first paragraph of text
  • One blank line before the image, one blank line after the caption
  • The alt text should describe the image content for screen readers
  • The caption should explain what the reader is looking at and credit the source

Caption format:

*[Description of what the image shows]. [Source attribution] ([License]).*

Examples:

markdown
*Big Bluestem grass towering in a tallgrass prairie. Photo: Jennifer Briggs / USFWS (CC BY 2.0).*
*EPA Level III Ecoregions of Minnesota. Source: U.S. Environmental Protection Agency (public domain).*

Step 6: Add Image Credits Section

Add an "Image Credits" section at the bottom of the chapter, before the "See Annotated References" link (or before "## Concepts Covered" if no references link exists):

markdown
## Image Credits

- [Brief description]: [Creator/Organization] ([License])
- [Brief description]: [Creator/Organization] ([License])

This serves as a consolidated attribution block for all images in the chapter, making license compliance easy to verify.

Step 7: Build and Verify

Run mkdocs build (or the project's build command) to verify:

  • No broken image paths
  • Images render at correct locations
  • GLightbox (if installed) enables click-to-zoom
  • Page load time is reasonable

License Compatibility Quick Reference

Source LicenseCompatible with CC BY-NC-SA 4.0?Attribution Required?
Public Domain (US Gov)YesNo (but good practice)
CC0YesNo
CC BY 2.0/3.0/4.0YesYes
CC BY-SA 2.0/3.0/4.0YesYes (SA carries through)
CC BY-NC 2.0/3.0/4.0YesYes
CC BY-NC-SAYes (same license)Yes
CC BY-NDNoN/A
CC BY-NC-NDNoN/A
All Rights ReservedNoN/A

Common Pitfalls

  1. Wikimedia download URLs: Never guess the hash path. Always use the Wikimedia API to get the actual URL. The hash path (/a/ab/File.jpg) is computed from the filename and is easy to get wrong.

  2. State government images: Unlike federal works, state government images are NOT automatically public domain. Check each state's terms of use.

  3. Flickr uploads on Commons: Many Wikimedia Commons photos were uploaded from Flickr. The license shown on Commons is what matters (it was verified at upload time), even if the Flickr page has since changed.

  4. Missing alt text: Every image must have descriptive alt text for accessibility. Describe what the image shows, not just its title.

  5. Oversized images: Always resize before committing. A 5MB photo will slow page loads significantly. Target 1200px max width and under 500KB.

  6. Portrait-oriented photos: Very tall images (portrait orientation) can dominate the page. Consider whether a landscape crop would work better, or use CSS to constrain the height.

Example Output

For a chapter about Minnesota ecoregions, the skill would produce:

5 images in docs/img/chapters/02-ecoregions/:

FileSourceSize
mn-ecoregion-map.pngEPA (public domain)583 KB
usda-hardiness-zones-mn.jpgUSDA-ARS (public domain)301 KB
tallgrass-prairie.jpgUSFWS via Wikimedia (CC BY 2.0)383 KB
deciduous-forest.jpgWikimedia Commons (CC BY-SA 2.0)432 KB
boreal-forest-bwca.jpgUS Forest Service (public domain)113 KB

5 insertions in the chapter markdown, each with alt text, caption, and attribution.

1 Image Credits section at the bottom of the chapter consolidating all attributions.

Frequently asked questions

What does the Chapter Image Enhancer AI skill do?

Adds freely-licensed maps and photos to textbook chapters by sourcing from Wikimedia Commons and US government archives, then inserting them with captions and proper attribution.

Why use Chapter Image Enhancer on TypingMind?

Because you install it once and use it with any model. Chapter Image Enhancer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chapter Image Enhancer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dmccreary/ibook-skills/tree/main/skills/archived/chapter-image-enhancer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chapter Image Enhancer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chapter Image Enhancer?

As many as you like. As long as a model supports skills, you can use Chapter Image Enhancer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chapter Image Enhancer AI skill free?

It is published on GitHub by dmccreary. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇