hub-swarm

name	hub-swarm
description	KTG content marketing pipeline — SWARM MODE. Orchestrates 7 plugins via parallel agent dispatch. Drop a post, get it published everywhere with maximum parallelism. Local-first: Ollama for text, cloud for images and live data. Agents run simultaneously, not sequentially.
user-invokable	true
argument-hint	<post-file-path> [--cloud] [--ads] [--canvas]
disable-model-invocation	false
metadata	{"author":"ktg","version":"2.2.0"}

Hub-Swarm: Parallel Content Pipeline

Drop a post. Spawn agents. Publish everywhere.

Local-First Routing

All text agents default to Ollama. Image + publish agents use cloud.

Swarm	Local (Ollama)	Cloud (Skill tool)	Default
Content	Ministral:latest (13.5B)	blog-repurpose	Local
SEO (5 agents)	Qwopus:latest (9B)	blog-geo, seo-page, etc.	Local
Ads brief	Qwopus:latest (9B)	ads-create	Local
Image hero	—	blog-image → banana	Cloud
Image crops	ImageMagick (local binary)	—	Local
Publish	—	Composio MCP	Cloud

Override: --cloud skips Ollama, uses Skill tool for all text tasks.

Architecture

PHASE 0: VALIDATE (Sequential — fast)
  └─ Validate input file exists, extract title/slug
  └─ Check Ollama: curl http://localhost:11434/api/tags

PHASE 1: SWARM DISPATCH (Parallel — all agents at once)
  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
  │ CONTENT     │ │ IMAGES      │ │ SEO         │
  │ ollama-     │ │ blog-image  │ │ ollama-geo  │
  │ repurpose   │ │ or banana   │ │ ollama-seo  │
  │ (5 agents)  │ │             │ │ (5 agents)  │
  └─────────────┘ └─────────────┘ └─────────────┘
  ┌─────────────┐ ┌─────────────┐
  │ ADS         │ │ CANVAS      │
  │ ollama-ads  │ │ canvas-     │
  │ (optional)  │ │ generate    │
  │             │ │ (optional)  │
  └─────────────┘ └─────────────┘

PHASE 2: GATHER (Sequential — collect results)
  └─ Wait for all agents. Assemble artifact manifest.

PHASE 3: REVIEW GATE (Sequential — human checkpoint)
  └─ Show manifest. Wait for explicit YES per channel.

PHASE 4: PUBLISH SWARM (Parallel — all channels at once)
  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
  │ VERCEL      │ │ WORDPRESS   │ │ SOCIAL      │
  │ deploy      │ │ publish     │ │ Reddit      │
  │             │ │             │ │ LinkedIn    │
  │             │ │             │ │ X           │
  └─────────────┘ └─────────────┘ └─────────────┘
  ┌─────────────┐
  │ ADS         │
  │ Google Ads  │
  │ Meta Ads    │
  │ LinkedIn    │
  │ TikTok      │
  └─────────────┘

---

Plugin → Skill Mapping

Swarm Agent	Actual Skill Name	Scope	Invocation
blog-repurpose	blog-repurpose	User (~/.agents/skills/)	Skill tool, prompt with file path + all platforms
blog-image	blog-image	User (~/.agents/skills/)	Skill tool, generate <topic>
banana	banana	Project (.claude/skills/)	Skill tool, generate <prompt>
blog-geo	blog-geo	User (~/.agents/skills/)	Skill tool, prompt with file path
seo-page	seo-page	User (~/.agents/skills/)	Skill tool, <url> or file path
seo-technical	seo-technical	User (~/.agents/skills/)	Skill tool, <url> or file path
seo-schema	seo-schema	User (~/.agents/skills/)	Skill tool, <url> or file path
blog-seo-check	blog-seo-check	User (~/.agents/skills/)	Skill tool, prompt with file path
ads-create	ads-create	User (~/.agents/skills/)	Skill tool, prompt with brand + post
ads-generate	ads-generate	User (~/.agents/skills/)	Skill tool, prompt with campaign brief
canvas-generate	canvas-generate	Project (.claude/skills/)	Skill tool, prompt with post content

---

Swarm Agent Definitions

Content Swarm

Default (local): 5 parallel Ollama agents, one per platform.

agent: ollama-repurpose-medium
model: Ministral:latest
parallel: true
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Ministral:latest","prompt":"Write a Medium article (800-1200 words,
  professional tone, discussion prompt) based on this post:\n<post-content-or-summary>\n
  Output only the article.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > social-medium.md
output: social-medium.md

agent: ollama-repurpose-reddit
model: Ministral:latest
parallel: true
task: >
  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 post:\n<post-content-or-summary>\n
  Output only the post.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > social-reddit.md
output: social-reddit.md

agent: ollama-repurpose-x
model: Ministral:latest
parallel: true
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Ministral:latest","prompt":"Write an X thread (hook + 8-15 tweets,
  engagement CTA) based on this post:\n<post-content-or-summary>\n
  Output only the thread, one tweet per line.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > social-x-thread.md
output: social-x-thread.md

agent: ollama-repurpose-meta
model: Ministral:latest
parallel: true
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Ministral:latest","prompt":"Write a Facebook/Meta post
  (scannable, key takeaways, CTA) based on this post:\n<post-content-or-summary>\n
  Output only the post.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > social-meta.md
output: social-meta.md

agent: ollama-repurpose-linkedin
model: Ministral:latest
parallel: true
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Ministral:latest","prompt":"Write a LinkedIn article (professional,
  data-forward, 800-1200 words, discussion prompt) based on this post:\n<post-content-or-summary>\n
  Output only the article.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > social-linkedin.md
output: social-linkedin.md

Cloud fallback (--cloud):

agent: blog-repurpose
skill_name: blog-repurpose
scope: user
parallel: true
task: >
  Use the Skill tool with name="blog-repurpose". Prompt:
  "Read the blog post at <file-path>. Repurpose it for ALL platforms:
  Medium, Reddit, X, Meta, LinkedIn. Generate all five variants."
output: social-*.md (5 files)

Image Swarm

agent: blog-image
skill_name: blog-image
scope: user
parallel: true
dependency: validate_complete
task: >
  Use the Skill tool with name="blog-image". Prompt:
  "Generate a hero image for a blog post titled '<title>'.
  Topic: <one-sentence summary>. Style: editorial, AI/tech theme, no text overlay.
  Aspect ratio: 16:9. Save to hero-16x9.png in the post directory."
fallback: >
  If blog-image MCP unavailable, use Skill tool with name="banana".
  Prompt: "Generate a hero image for '<title>'. <summary>. Style: editorial, AI/tech.
  Aspect ratio: 16:9. Size: 2K. Save to hero-16x9.png."
output: hero-16x9.png

agent: image-crop
skill_name: bash (ImageMagick)
scope: local
parallel: true
dependency: hero_generated
task: >
  Run ImageMagick crops via Bash:
  magick hero-16x9.png -resize 1200x627^ -gravity Center -extent 1200x627 hero-linkedin.png
  magick hero-16x9.png -resize 1200x675^ -gravity Center -extent 1200x675 hero-x.png
  magick hero-16x9.png -resize 1080x1080^ -gravity Center -extent 1080x1080 hero-square.png
output: hero-*.png (3 files)

SEO Swarm

Default (local): 5 parallel Ollama agents.

agent: ollama-geo
model: Qwopus:latest
parallel: true
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Qwopus:latest","prompt":"You are a GEO analyst. Score this post
  for AI citation readiness (ChatGPT, Perplexity, Google AIO).\n<post-frontmatter + first 2000 words + headings>\n
  Rate 0-100 on: Passage Citability, Structural Readability, Authority Signals,
  Technical Accessibility, Freshness. Output GEO Readiness Score: XX/100
  with platform breakdown and top 5 changes.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > GEO-ANALYSIS.md
output: GEO-ANALYSIS.md

agent: ollama-seo-page
model: Qwopus:latest
parallel: true
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Qwopus:latest","prompt":"You are an SEO auditor. Analyze this post
  for on-page SEO and content quality.\n<post-frontmatter + first 2000 words + headings>\n
  Score 0-100 per: On-Page SEO, Content Quality, Technical Meta, Images.
  Output overall score with category breakdown and prioritized fixes.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > SEO-PAGE.md
output: SEO-PAGE.md

agent: ollama-seo-technical
model: Qwopus:latest
parallel: true
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Qwopus:latest","prompt":"You are a technical SEO auditor.
  This is a static Markdown file (not a live site). Audit:\n<post-frontmatter + headings + links + image refs>\n
  Check: heading hierarchy, internal links (3+), external links (2+),
  image alt text, URL slug, canonical, OG hints, mobile readiness.
  Output Technical Score: XX/100 with fixes.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > SEO-TECHNICAL.md
output: SEO-TECHNICAL.md

agent: ollama-schema
model: Ministral:latest
parallel: true
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Ministral:latest","prompt":"Generate valid JSON-LD schema.org
  for this blog post.\nFrontmatter: <frontmatter>\nTitle: <title>\nAuthor: <author>\nPublished: <date>\nModified: <date>\nURL: <canonical>\n
  Required: Article/BlogPosting, Organization, Person, BreadcrumbList.
  Output ONLY valid JSON-LD inside ```json ... ``` blocks.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > schema.json
output: schema.json

agent: ollama-seo-check
model: Qwopus:latest
parallel: true
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Qwopus:latest","prompt":"Run post-writing SEO validation checklist.\n<full post>\n
  Check: title (40-60 chars), meta (150-160), H1 (exactly one), hierarchy (no skips),
  internal links (3+), external links (2+), canonical, OG, Twitter Cards,
  URL slug, image alt text. Output pass/fail per item with fixes.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > SEO-CHECKLIST.md
output: SEO-CHECKLIST.md

Cloud fallback (--cloud):

agent: blog-geo
skill_name: blog-geo
scope: user
parallel: true
task: >
  Use Skill tool with name="blog-geo". Prompt: "Score <file-path> for AI citation readiness."
output: GEO-ANALYSIS.md

agent: seo-page
skill_name: seo-page
scope: user
parallel: true
task: >
  Use Skill tool with name="seo-page". Prompt: "Run on-page SEO analysis on <file-path>."
output: SEO score card

agent: seo-technical
skill_name: seo-technical
scope: user
parallel: true
task: >
  Use Skill tool with name="seo-technical". Prompt: "Run technical SEO audit on <file-path>."
output: Technical score card

agent: seo-schema
skill_name: seo-schema
scope: user
parallel: true
task: >
  Use Skill tool with name="seo-schema". Prompt: "Generate JSON-LD for <file-path>."
output: schema.json

agent: blog-seo-check
skill_name: blog-seo-check
scope: user
parallel: true
task: >
  Use Skill tool with name="blog-seo-check". Prompt: "Run SEO validation on <file-path>."
output: Pass/fail checklist

Ads Swarm (Optional)

Default (local):

agent: ollama-ads-create
model: Qwopus:latest
parallel: true
condition: user_requested_ads
dependency: validate_complete
task: >
  curl -s http://localhost:11434/api/generate
  -d '{"model":"Qwopus:latest","prompt":"You are an ad strategist. Read this blog post
  and create a campaign brief.\n<post-content-or-summary>\n
  Output: campaign concept, messaging pillars, target audience, 3 headline options,
  2 CTA options, recommended platforms. Save as campaign-brief.md.","stream":false}'
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])"
  > campaign-brief.md
output: campaign-brief.md

Ad creatives use cloud (blog-image or banana) — no local image gen model.

Cloud fallback (--cloud):

agent: ads-create
skill_name: ads-create
scope: user
parallel: true
task: >
  Use Skill tool with name="ads-create". Prompt:
  "Read brand-profile.json and <file-path>. Produce campaign brief."
output: campaign-brief.md

agent: ads-generate
skill_name: ads-generate
scope: user
parallel: true
dependency: campaign-brief_complete
task: >
  Use Skill tool with name="ads-generate". Prompt:
  "Read campaign-brief.md and brand-profile.json. Produce ad creatives."
output: ad-creative-*.png

Canvas Swarm (Optional)

agent: canvas-generate
skill_name: canvas-generate
scope: project
parallel: true
condition: user_requested_visual_planning
dependency: validate_complete
task: >
  Use the Skill tool with name="canvas-generate". Prompt:
  "Create a visual canvas from the blog post at <file-path>. Detect the best
  archetype, generate content and visuals, instantiate template, apply layout,
  produce complete canvas. Save to post.canvas in post directory."
output: post.canvas

---

Dependency Graph

validate_input
    ├── ollama-repurpose-* (5 agents) ──┐
    │                                   ├──→ ollama-seo-* (5 agents, parallel)
    ├── blog-image/banana               │   ├──→ ollama-ads-create
    │                                   │   │
    └── image-crop (ImageMagick)        ├───┼──→ (no dependency on ads)
                                        │   │
                                        └───┴──→ canvas-generate (optional)

---

Review Gate Manifest

HUB-SWARM — READY TO PUBLISH

CONTENT:
  Post: <title>
  X thread: <word_count> words
  LinkedIn: <word_count> words
  Reddit: <word_count> words
  Medium: <word_count> words
  Meta: <word_count> words

IMAGES:
  Hero 16:9, LinkedIn crop, X crop, Square crop

SEO:
  GEO score: <N>/100
  SEO score: <N>/100
  Technical score: <N>/100
  Schema: <types>
  SEO check: <pass/fail count>

ADS (if requested):
  Google Ads: <headline_count> headlines
  Meta Ads: <headline_count> headlines
  LinkedIn Ads: <headline_count> headlines
  TikTok Ads: <headline_count> headlines

CANVAS (if requested):
  <yes/no> Visual planning

CHANNELS:
  Vercel, WordPress, Reddit, LinkedIn, X, Meta, Medium

Type YES to publish all, SKIP <channel> to exclude, STOP to cancel.

---

Performance

Local Ollama runs at ~20-40 tokens/sec on RTX-class GPU. No API latency.

Phase	Sequential (cloud)	Swarm (cloud)	Swarm (local)	Speedup
Validate	1s	1s	1s (+ Ollama check)	1x
Content (5 variants)	300s	60s	60s	5x
Images	30s	30s	30s	1x
SEO (5 agents)	225s	45s	45s	5x
Ads	60s	20s	20s	3x
Canvas	30s	30s	30s	optional
Total	~646s	~186s	~186s	3.5x

Cost: Local = $0 in API calls. Cloud = ~$0.50-2.00 per run depending on post length.

---

Error Handling

• Agent timeout: Report which agent/skill timed out. Continue with others.
• Agent failure: Report failure. Offer to retry that agent only.
• Partial swarm: If 4 of 5 SEO agents succeed, use partial results.
• Ollama not running: Check curl http://localhost:11434/api/tags. If fails, auto-fallback to --cloud mode.
• Ollama model missing: Auto-pull with ollama pull <model> or fallback to --cloud.
• Context overflow: If post >8k words, chunk into summaries before sending to Ollama.
• Dependency failure: If blog-image/banana fails, image-crop and ads-generate skip gracefully.
• ImageMagick missing: Skip crops, note in review gate, continue.

---

Notes

• Local-first by default: Ollama for text, cloud for images + publish
• Ollama models: Ministral:latest (13.5B) for content/schema, Qwopus:latest (9B) for SEO/GEO/analysis
• Swarm uses the Agent tool with subagent_type: general for parallel execution
• Each agent gets its own context budget — no shared state
• Results gathered via manifest, not conversation memory
• Review gate is the only sequential bottleneck (by design)
• Post-publish: wiki log entry, hot cache update
• Cloud skill invocations use the Skill tool with exact name= from the Plugin Map
• Never Read skill files directly — invoke via Skill tool only