Imported from ktg-one/marketing (.claude/skills/hub/SKILL.md). Install upstream with npx skills add ktg-one/marketing --skill hub. Copyright stays with the author.

Hub — KTG Content Marketing Pipeline

Drop a post file. Get it published everywhere.

Local-First Routing

Override: Pass --cloud to skip local and use cloud skills for all text tasks.

Ollama Invocation Pattern

# Check server
curl -s http://localhost:11434/api/tags | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models']]"

# Generate (non-streaming, for agents)
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"<prompt>","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"

# Chat (multi-turn)
curl -s http://localhost:11434/api/chat \
  -d '{"model":"Ministral:latest","messages":[{"role":"user","content":"<prompt>"}],"stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['message']['content'])"

# Embeddings
curl -s http://localhost:11434/api/embeddings \
  -d '{"model":"qwen3-embedding:latest","prompt":"<text>"}' \
  | python3 -c "import sys,json; print(len(json.load(sys.stdin)['embedding']))"

Context window: Ministral 13.5B = ~32k tokens. For posts >10k words, chunk into 8k-token segments.

Plugin Map

Step 0 — Validate input

Parse $ARGUMENTS for a file path. If none provided:

Usage: /hub <path-to-post.md>

Example: /hub wiki/content/mirage-post/post.md

Read the post file. Extract:

• Title — from frontmatter title: or first H1
• Slug — kebab-case title for output file naming
• Platform — detect from frontmatter platform: if present (default: generic blog)

Report: Running KTG hub pipeline on: <title>

Step 1 — Repurpose

Default (local): Generate variants via Ollama Ministral:latest.

Read the post file into memory. If >8k words, extract key points (title, thesis, 3 main arguments, 2 key stats, CTA) and pass the summary instead of full text.

Run 5 Ollama calls in sequence (or parallel via Agent tool):

# Medium
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Write a Medium article (800-1200 words, professional tone, discussion prompt at end) based on this blog post:\n\n<post-content-or-summary>\n\nOutput only the article, no preamble.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-medium.md

# Reddit
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Write a Reddit discussion post (authentic tone, r/ClaudeAI format, discussion starter) based on this blog post:\n\n<post-content-or-summary>\n\nOutput only the post, no preamble.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-reddit.md

# X thread
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Write an X/Twitter thread (hook tweet + 8-15 tweets, engagement CTA, match character limits) based on this blog post:\n\n<post-content-or-summary>\n\nOutput only the thread, one tweet per line, no preamble.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-x-thread.md

# Meta
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Write a Facebook/Meta post (scannable, key takeaways, CTA) based on this blog post:\n\n<post-content-or-summary>\n\nOutput only the post, no preamble.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-meta.md

# LinkedIn
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Write a LinkedIn article (professional tone, data-forward, 800-1200 words, discussion prompt) based on this blog post:\n\n<post-content-or-summary>\n\nOutput only the article, no preamble.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-linkedin.md

Cloud fallback (--cloud or if Ollama not running): Use the Skill tool with name="blog-repurpose":

Read the blog post at <file-path>. Repurpose it for ALL platforms: Medium article, Reddit discussion post, X thread, Meta post, and LinkedIn article. Generate all five variants in one invocation. Match platform-native tone and formatting. Save each variant to the same directory as the post using these exact filenames:

• social-medium.md
• social-reddit.md
• social-x-thread.md
• social-meta.md
• social-linkedin.md

Report: Repurpose complete — 5 variants generated

Step 2 — Images

Generate hero image:

Use the Skill tool with name="blog-image" and pass:

Generate a hero image for a blog post titled "". The post is about . Style: editorial, AI/tech theme, no text overlay. Aspect ratio: 16:9. Save to hero-16x9.png in the post directory.

If blog-image MCP is unavailable, fall back to Skill tool with name="banana":

Generate a hero image for a blog post titled "". The post is about . Style: editorial, AI/tech theme, no text overlay. Aspect ratio: 16:9. Size: 2K. Save to hero-16x9.png in the post directory.

Crop for platforms (ImageMagick — run via Bash):

# LinkedIn / OG: 1200x627
magick "hero-16x9.png" -resize 1200x627^ -gravity Center -extent 1200x627 "hero-linkedin.png"

# Twitter/X: 1200x675
magick "hero-16x9.png" -resize 1200x675^ -gravity Center -extent 1200x675 "hero-x.png"

# Square (Reddit/Instagram): 1080x1080
magick "hero-16x9.png" -resize 1080x1080^ -gravity Center -extent 1080x1080 "hero-square.png"

If ImageMagick is not installed, report: "ImageMagick not found — skipping crops. Install with winget install ImageMagick.ImageMagick then re-run."

Report: Images complete — hero + 3 crops

Step 3 — Optimise

Default (local): Run audits via Ollama. All prompts extract the post's frontmatter + first 2000 words + heading structure to stay within context limits.

3a. AI citation / GEO optimisation (local):

curl -s http://localhost:11434/api/generate \
  -d '{"model":"Qwopus:latest","prompt":"You are a GEO (Generative Engine Optimization) analyst. Score this blog post for AI citation readiness (ChatGPT, Perplexity, Google AI Overviews).\n\nPost content:\n<post-frontmatter + first 2000 words + all headings>\n\nRate 0-100 on:\n1. Passage-Level Citability (clear 120-180 word answer blocks)\n2. Structural Readability (H1-H2-H3 hierarchy, question headings, lists/tables)\n3. Authority Signals (author byline, dates, sources cited)\n4. Technical Accessibility (AI crawler hints, no JS-gated content)\n5. Freshness (update dates within 30 days)\n\nOutput format:\nGEO Readiness Score: XX/100\nPlatform Breakdown: Google AIO: X/100 | ChatGPT: X/100 | Perplexity: X/100\nTop 5 Changes: 1. ... 2. ... 3. ... 4. ... 5. ...","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > GEO-ANALYSIS.md

3b. SEO audit (local):

# On-page + content quality
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Qwopus:latest","prompt":"You are an SEO auditor. Analyze this blog post for on-page SEO and content quality.\n\nPost content:\n<post-frontmatter + first 2000 words + all headings>\n\nScore 0-100 per category:\n- On-Page SEO (title, meta, H1, headings, URL, internal/external links)\n- Content Quality (word count, readability Flesch 60-70, keyword density 1-3%, E-E-A-T)\n- Technical Meta (canonical, OG tags, Twitter Cards, hreflang)\n- Images (alt text, file size, format, dimensions, lazy loading)\n\nOutput: Overall Score: XX/100 with category breakdown and prioritized fixes.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > SEO-ANALYSIS.md

# Technical SEO (analyzed as static markdown file, not live site)
curl -s http://localhost:11434/api/generate \
  -d '{"model":"Qwopus:latest","prompt":"You are a technical SEO auditor. This is a static Markdown blog post (not a live site). Audit what CAN be checked from the file:\n\nPost content:\n<post-frontmatter + all headings + links + image refs>\n\nCheck:\n1. Heading hierarchy (no skipped H1->H2->H3)\n2. Internal links (3+ contextual, descriptive anchor)\n3. External links (2+ authoritative, Tier 1-2)\n4. Image alt text (present on all images)\n5. URL slug (lowercase, hyphens, <60 chars)\n6. Canonical hint (if frontmatter has canonical)\n7. OG/twitter card hints (if frontmatter has them)\n8. Mobile readiness (short paragraphs, no tables wider than screen)\n\nOutput: Technical Score: XX/100 with fixes.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" >> SEO-ANALYSIS.md

3c. Schema markup (local):

curl -s http://localhost:11434/api/generate \
  -d '{"model":"Ministral:latest","prompt":"Generate valid JSON-LD schema.org markup for this blog post.\n\nPost frontmatter:\n<frontmatter>\n\nPost title: <title>\nAuthor: <author or \"Kevin Tan\">\nPublished: <date>\nModified: <date>\nURL: <canonical or placeholder>\n\nRequired schemas:\n1. Article (or BlogPosting) — headline, author, datePublished, dateModified, image, publisher\n2. Organization — name, url, logo, sameAs\n3. Person (author) — name, url, sameAs\n4. BreadcrumbList — if applicable\n\nOutput ONLY valid JSON-LD inside ```json ... ``` blocks. No commentary.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > schema.json

3d. Post-writing SEO validation (local):

curl -s http://localhost:11434/api/generate \
  -d '{"model":"Qwopus:latest","prompt":"Run a post-writing SEO validation checklist on this blog post.\n\nPost content:\n<full post>\n\nChecklist (pass/fail + fix if fail):\n□ Title tag: 40-60 chars, primary keyword in first half, power word\n□ Meta description: 150-160 chars, statistic included, value prop at end\n□ H1: exactly one, matches intent, includes keyword\n□ Heading hierarchy: no skipped levels (H1->H2->H3 only)\n□ Internal links: 3+ contextual, descriptive anchor\n□ External links: 2+ authoritative sources\n□ Canonical URL: set in frontmatter\n□ OG tags: og:title, og:description, og:image present\n□ Twitter Cards: twitter:card, twitter:image present\n□ URL slug: lowercase, hyphens, <60 chars\n□ Image alt text: present on all images\n\nOutput: Checklist with pass/fail per item and specific fixes.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > SEO-CHECKLIST.md

Cloud fallback (--cloud or if Ollama not running): Run these skill invocations in sequence:

3a-cloud. Use Skill tool with name="blog-geo":

Score the blog post at <file-path> for AI citation readiness across ChatGPT, Perplexity, and Google AI Overviews. Output a 0-100 score and platform-specific recommendations. Save the analysis as GEO-ANALYSIS.md in the post directory.

3b-cloud. Use Skill tool with name="seo-page":

Run a deep single-page SEO analysis on the blog post at <file-path>. Cover on-page elements, content quality, technical meta tags, schema, images, and performance. Report the overall score and critical issues.

Then use Skill tool with name="seo-technical":

Run a technical SEO audit on the blog post at <file-path>. Cover crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow. Report the technical score and critical issues.

3c-cloud. Use Skill tool with name="seo-schema":

Detect and validate schema markup for the blog post at <file-path>. Generate missing JSON-LD for Article/BlogPosting, Organization, Person, and BreadcrumbList. Save to schema.json in the post directory.

3d-cloud. Use Skill tool with name="blog-seo-check":

Run the post-writing SEO validation checklist on the blog post at <file-path>. Check title tag, meta description, heading hierarchy, internal/external links, canonical URL, OG tags, Twitter Cards, URL structure, and image alt text. Report pass/fail per item with fix recommendations.

Report: Optimisation complete — GEO score: <N>/100, SEO score: <N>/100, schema generated

Step 4 — Review gate (MANDATORY)

STOP. Do not publish anything yet.

Present a full summary to the user:

═══════════════════════════════════════
  KTG HUB — READY TO PUBLISH
═══════════════════════════════════════

Post: <title>
File: <path>

VARIANTS GENERATED:
  ✓ Medium         → social-medium.md
  ✓ Reddit         → social-reddit.md
  ✓ X thread       → social-x-thread.md
  ✓ Meta           → social-meta.md
  ✓ LinkedIn       → social-linkedin.md

IMAGES:
  ✓ Hero 16:9      → hero-16x9.png
  ✓ LinkedIn crop  → hero-linkedin.png
  ✓ X crop         → hero-x.png
  ✓ Square crop    → hero-square.png

OPTIMISATION:
  ✓ GEO pass       → GEO-ANALYSIS.md
  ✓ SEO audit      → SEO-ANALYSIS.md
  ✓ SEO checklist  → SEO-CHECKLIST.md
  ✓ Schema         → schema.json

CHANNELS:
  → Reddit r/ClaudeAI
  → LinkedIn personal feed
  → Vercel deploy (canonical URL first)

═══════════════════════════════════════

Then ask:

Review the variants before publishing. Type YES to publish to all channels, SKIP to exclude a channel, or STOP to cancel.

Do not proceed until explicit YES is received.

Step 5 — Publish

5a. Vercel deploy first (get canonical URL):

Use Composio to deploy the post to Vercel. Capture the canonical URL from the deploy response.

Substitute the canonical URL into all social variants where [CANONICAL_URL] placeholder appears.

5b. Fire each channel:

Use Composio MCP tools to publish:

For each channel, report success or failure. If a channel fails, report the error and continue with remaining channels — do not abort the whole pipeline.

5c. Final report:

═══════════════════════════════════════
  KTG HUB — PUBLISHED
═══════════════════════════════════════

  ✓ Vercel    → <canonical-url>
  ✓ Medium    → <post-url>
  ✓ Reddit    → <post-url>
  ✓ X         → <post-url>
  ✓ Meta      → <post-url>
  ✓ LinkedIn  → <post-url>

═══════════════════════════════════════

5d. Wiki log: After successful publish, append to wiki/log.md:

- $(date +%Y-%m-%d) — Published: <title> → <canonical-url>

Update wiki/hot.md with the published URL and status.

Error handling

• Missing file: Report path not found, stop.
• Skill fails: Report which skill name failed, offer to retry that step only. Use the exact skill name from the Plugin Map.
• Ollama not running: Check curl http://localhost:11434/api/tags. If fails, fall back to --cloud mode for all text tasks.
• Ollama model missing: If requested model not installed, run ollama pull <model> or fall back to --cloud.
• Ollama context overflow: If post >8k words, chunk into summaries. Ministral context = ~32k tokens.
• ImageMagick missing: Skip crops, note in review gate, continue.
• Composio channel fails: Report error, continue other channels.
• User types STOP at gate: Cancel cleanly. All generated files are kept.

Notes

• The review gate is non-negotiable — never auto-publish.
• Local-first by default: Ollama handles text tasks (repurpose, SEO, GEO, schema). Cloud skills only for --cloud, image gen, live data, and publishing.
• Ollama models installed: Ministral:latest (13.5B), Qwopus:latest (9B), QwenVL:latest (8.2B), qwen3-embedding:latest (7.6B)
• Composio connections active: medium, reddit, x, meta, linkedin, vercel
• For Windows/PowerShell: ImageMagick commands use magick not convert
• Post variants are saved alongside the original — the wiki auto-commit hook picks them up
• Skill invocations use the Skill tool with exact name= from the Plugin Map. Never Read skill files directly.