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Blog Taxonomy

CommunityPopular
AgriciDaniel
blog-taxonomy

Extract, suggest, and sync tags and categories for blog posts across all major CMS platforms. Supports WordPress REST API, Shopify GraphQL, Ghost Content API, Strapi REST/GraphQL, and Sanity GROQ. Generates tag suggestions from content analysis (keyword frequency, heading extraction, semantic grouping), enforces minimum post-count thresholds to prevent thin tag archives, and syncs taxonomy via authenticated API calls. Use when user says "tags", "categories", "taxonomy", "tag suggestions", "sync tags", "WordPress tags", "Shopify tags".

Overview

PublisherAgriciDaniel
Repositoryclaude-blog
Skill nameblog-taxonomy
Stars
2.2K
Forks
362
Bundled files
Instructions only
LicenseMIT
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 AgriciDaniel on GitHub. Read the source before you install it.

Installation

Install the Blog Taxonomy 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.
  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/AgriciDaniel/claude-blog.git /tmp/claude-blog
mkdir -p .claude/skills
cp -r /tmp/claude-blog/skills/blog-taxonomy .claude/skills/blog-taxonomy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blog Taxonomy 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 Blog Taxonomy 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 Blog Taxonomy 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.

Blog Taxonomy

Manage tags, categories, and topic clusters across CMS platforms.

Commands

CommandPurpose
/blog taxonomy suggest <file>Extract candidate tags and categories from content
/blog taxonomy sync <cms>Push taxonomy to CMS via authenticated API
/blog taxonomy audit [directory]Check for thin tags, orphan tags, taxonomy bloat

Tag Suggestion Workflow

Step 1: Parse Content Structure

Read the target file and extract:

  • All H2 and H3 headings (primary topic signals)
  • Bold and italic phrases (emphasis signals)
  • Existing frontmatter tags/categories if present

Step 2: Frequency Analysis

Scan the body text for high-frequency phrases:

  • 1-word terms: minimum 4 occurrences (excluding stop words)
  • 2-word phrases: minimum 3 occurrences
  • 3-word phrases: minimum 2 occurrences

Exclude common non-tag words: articles, prepositions, conjunctions, pronouns.

Step 3: Semantic Grouping

Group related candidates into clusters:

  • Merge singular/plural variants (keep the more common form)
  • Merge hyphenated and non-hyphenated forms
  • Group synonyms under the highest-frequency term

Step 4: Deduplicate and Rank

  • Fuzzy match on slugified names (Levenshtein distance <= 2)
  • Do not auto-merge short slugs under 5 characters using Levenshtein alone; require token overlap or manual review
  • Score each candidate: (frequency * 2) + (heading_presence * 5) + (emphasis * 1)
  • Return top 5-10 ranked suggestions

Output Format

## Tag Suggestions: [Post Title]

| Rank | Tag | Score | Source |
|------|-----|-------|--------|
| 1 | content-marketing | 18 | H2 + 6 mentions |
| 2 | seo-strategy | 14 | H3 + 4 mentions |
| 3 | keyword-research | 11 | 5 mentions + bold |

### Suggested Categories
- Primary: [best-fit category]
- Secondary: [optional second category]

CMS Adapters

Adapter Overview

CMSAPI TypeAuth MethodTags Model
WordPressRESTApplication Passwords (base64)First-class entities with IDs
ShopifyGraphQL (Admin API)Admin API access tokenString array on Article
GhostREST (Admin API)API key with JWT signingFirst-class entities
StrapiREST or GraphQLAPI token (Bearer)User-defined content type
SanityGROQ / MutationsProject token (Bearer)Document type

WordPress Adapter

List tags:

GET {CMS_URL}/wp-json/wp/v2/tags?per_page=100&search={keyword}
Authorization: Basic {base64(username:app_password)}

Create tag:

POST {CMS_URL}/wp-json/wp/v2/tags
Body: {"name": "Tag Name", "slug": "tag-name", "description": "Optional"}

List categories (hierarchical, supports parent field):

GET {CMS_URL}/wp-json/wp/v2/categories?per_page=100

Create category:

POST {CMS_URL}/wp-json/wp/v2/categories
Body: {"name": "Category", "slug": "category", "parent": 0}

Assign tags to post:

POST {CMS_URL}/wp-json/wp/v2/posts/{id}
Body: {"tags": [1, 2, 3], "categories": [4]}

Pagination: follow X-WP-TotalPages header for full listing.

Shopify Adapter

Tags on Shopify are string arrays on the Article object, not first-class entities.

Update article tags (GraphQL Admin API):

graphql
mutation {
  articleUpdate(id: "gid://shopify/Article/123", article: {
    tags: ["tag-one", "tag-two", "tag-three"]
  }) {
    article { id tags }
    userErrors { field message }
  }
}

List all tags in use (GraphQL):

graphql
{
  articles(first: 250, after: $cursor) {
    pageInfo { hasNextPage endCursor }
    edges {
      node { id title tags }
    }
  }
}

Auth header: X-Shopify-Access-Token: {token}

Pagination: loop while pageInfo.hasNextPage is true, passing endCursor as the next $cursor.

Note: REST API marked legacy Oct 2024. GraphQL required for new apps since Apr 2025.

Ghost Adapter

List tags:

GET {CMS_URL}/ghost/api/admin/tags/?limit=all
Authorization: Ghost {jwt_token}

Create tag:

POST {CMS_URL}/ghost/api/admin/tags/
Body: {"tags": [{"name": "Tag Name", "slug": "tag-name"}]}

JWT generation: sign with admin API key (id:secret format), iat = now, exp = 5 min, audience = /admin/.

Strapi Adapter

Endpoint auto-generated from content types. Typical setup:

GET {CMS_URL}/api/tags?pagination[pageSize]=100
POST {CMS_URL}/api/tags
Body: {"data": {"name": "Tag Name", "slug": "tag-name"}}
Authorization: Bearer {api_token}

Pagination: increment pagination[page] until all pages are exhausted.

Strapi v4 responses use the data wrapper with attributes; Strapi v5 uses a flatter response shape. Detect the version or normalize both shapes before deduplication. Check your content type schema for field names.

Sanity Adapter

Query tags (GROQ):

*[_type == "tag"] { _id, name, slug }

Create tag (Mutations API):

POST https://{project_id}.api.sanity.io/{SANITY_API_VERSION}/data/mutate/{dataset}
Body: {"mutations": [{"create": {"_type": "tag", "name": "Tag", "slug": {"current": "tag"}}}]}
Authorization: Bearer {token}

Default SANITY_API_VERSION to a current tested API date supplied by the project environment; do not hard-code it in generated requests.

Taxonomy Audit Workflow

Step 1: Inventory

Scan all posts in the target directory (or fetch from CMS). Build a map:

  • tag_name -> [list of post files/IDs using this tag]
  • category_name -> [list of post files/IDs]

Step 2: Health Checks

CheckThresholdAction
Thin tag archives< 5 posts per tagReview for merge or noindex after traffic, intent, and link checks
Orphan tags0 postsRecommend deletion
Tag bloatMore than max(50, post_count * 0.25) total tags, adjusted for taxonomy purposeRecommend consolidation
Category depth> 3 levelsRecommend flattening
Uncategorized postsNo category assignedAssign to appropriate category
Duplicate slugsSame slug, different nameMerge into canonical version

Step 3: Recommendations

Group findings by priority:

  • Critical: orphan tags creating empty archive pages (crawl waste)
  • High: thin tags with < 5 posts after traffic, intent, and link checks
  • Medium: tag bloat above the scaled threshold (diluted taxonomy, harder to navigate)
  • Low: naming inconsistencies (mixed case, hyphen vs space)

Output Format

## Taxonomy Audit: [Site/Directory]

**Total tags**: [n] | **Total categories**: [n]
**Healthy**: [n] | **Thin**: [n] | **Orphan**: [n]

### Critical Issues
- [orphan tags list]

### Recommendations
1. Merge [tag-a] and [tag-b] (same topic, [n] combined posts)
2. Delete orphan tags: [list]
3. Merge or noindex tag archives with < 5 posts only after traffic, intent, and link checks

Site-Wide Guidelines

  • Aim for 5-10 main categories per site (broad topics)
  • Tags should have at least 5 posts before creating an archive page
  • Use consistent slug format: lowercase, hyphen-separated
  • Every post needs exactly 1 primary category
  • Tags per post: 3-8 recommended, never exceed 15

Environment Variables

VariablePurposeExample
CMS_TYPEPlatform identifierwordpress, shopify, ghost, strapi, sanity
CMS_URLHTTPS base URL of the CMShttps://example.com
CMS_ALLOWED_HOSTSOptional comma-separated allowlist for CMS hostsexample.com,admin.example.com
CMS_USERNAMEWordPress username when using Application Passwordseditor@example.com
CMS_API_KEYAuthentication credentialWordPress app password, API token, or key
SANITY_API_VERSIONSanity API date for mutationsv2026-07-01

These must be set in the shell environment. Never store credentials in files or commit them to version control. The skill reads them via $CMS_TYPE, $CMS_URL, $CMS_USERNAME, $CMS_API_KEY, and optional platform-specific variables at runtime.

Security rule for CMS calls: require HTTPS, allow only http and https parsing paths but send authenticated requests over HTTPS only, resolve DNS and block loopback/private/link-local/reserved IPs, validate redirects with the same checks or disable redirects, cap timeouts at 10 seconds, and enforce CMS_ALLOWED_HOSTS when set.

Error Handling

  • Missing environment variables: If CMS_TYPE, CMS_URL, or CMS_API_KEY is unset, or if WordPress lacks CMS_USERNAME, report which variable is missing and provide the expected format
  • Invalid credentials: If the CMS API returns 401/403, report "Authentication failed - check CMS_USERNAME/CMS_API_KEY" and do not retry
  • Connection timeouts: If the CMS endpoint is unreachable after 10 seconds, report the timeout and suggest checking CMS_URL
  • Duplicate tag slugs: If a tag already exists on the CMS, skip creation and note "Tag already exists: [name]"
  • Rate limits: If the CMS API returns 429, honor Retry-After when present; otherwise use exponential backoff and retry once. Report if the limit persists
  • Unsupported CMS: If CMS_TYPE is not one of the 5 supported platforms, list the valid options and exit

Frequently asked questions

What does the Blog Taxonomy AI skill do?

Extract, suggest, and sync tags and categories for blog posts across all major CMS platforms. Supports WordPress REST API, Shopify GraphQL, Ghost Content API, Strapi REST/GraphQL, and Sanity GROQ. Generates tag suggestions from content analysis (keyword frequency, heading extraction, semantic grouping), enforces minimum post-count thresholds to prevent thin tag archives, and syncs taxonomy via authenticated API calls. Use when user says "tags", "categories", "taxonomy", "tag suggestions", "sync tags", "WordPress tags", "Shopify tags".

Why use Blog Taxonomy on TypingMind?

Because you install it once and use it with any model. Blog Taxonomy 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 Blog Taxonomy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgriciDaniel/claude-blog/tree/main/skills/blog-taxonomy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Blog Taxonomy?

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 Blog Taxonomy?

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

Is the Blog Taxonomy AI skill free?

Yes. It is published on GitHub by AgriciDaniel under the MIT license. 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.

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