hub-swarm-v2

name	hub-swarm-v2
description	KTG content marketing pipeline — SWARM MODE v2. Concrete 11-agent parallel pipeline using the Agent tool. Local-first: Ollama for text, cloud for images and publish. Drop a post, spawn agents, publish everywhere.
user-invokable	true
argument-hint	<post-file-path> [--cloud] [--ads] [--canvas]
disable-model-invocation	false
metadata	{"author":"ktg","version":"2.2.0"}

Hub-Swarm v2 — Concrete Agentic Pipeline

Spawns 11 sub-agents in parallel using the Agent tool. Default: Ollama for text tasks. Cloud only for images, live data, and publishing.

Local-First Routing

#	Agent	Default	Model	Fallback
1	repurpose-medium	Local	Ministral:latest	blog-repurpose
2	repurpose-reddit	Local	Ministral:latest	blog-repurpose
3	repurpose-x	Local	Ministral:latest	blog-repurpose
4	repurpose-meta	Local	Ministral:latest	blog-repurpose
5	repurpose-linkedin	Local	Ministral:latest	blog-repurpose
6	image-hero	Cloud	—	blog-image → banana
7	seo-geo	Local	Qwopus:latest	blog-geo
8	seo-page	Local	Qwopus:latest	seo-page
9	seo-technical	Local	Qwopus:latest	seo-technical
10	seo-schema	Local	Ministral:latest	seo-schema
11	seo-check	Local	Qwopus:latest	blog-seo-check
12	ads-create	Local	Qwopus:latest	ads-create
13	ads-generate	Cloud	—	ads-generate
14	canvas-generate	Cloud	—	canvas-generate

Override: --cloud forces all text agents to use Skill tool instead of Ollama.

Agent Manifest

#	Agent Name	Invokes	Task	Output
1	repurpose-medium	Ollama Ministral:latest	Generate Medium variant	social-medium.md
2	repurpose-reddit	Ollama Ministral:latest	Generate Reddit variant	social-reddit.md
3	repurpose-x	Ollama Ministral:latest	Generate X thread variant	social-x-thread.md
4	repurpose-meta	Ollama Ministral:latest	Generate Meta variant	social-meta.md
5	repurpose-linkedin	Ollama Ministral:latest	Generate LinkedIn variant	social-linkedin.md
6	image-hero	blog-image (or banana)	Generate hero 16:9	hero-16x9.png
7	seo-geo	Ollama Qwopus:latest	AI citation audit	GEO-ANALYSIS.md + score
8	seo-page	Ollama Qwopus:latest	On-page SEO audit	SEO-PAGE.md
9	seo-technical	Ollama Qwopus:latest	Technical SEO audit	SEO-TECHNICAL.md
10	seo-schema	Ollama Ministral:latest	JSON-LD generation	schema.json
11	seo-check	Ollama Qwopus:latest

Optional agents (if --ads): | 12 | ads-create | Ollama Qwopus:latest | Campaign brief | campaign-brief.md | | 13 | ads-generate | ads-generate | Ad creatives | ad-creative-*.png |

Optional agents (if --canvas): | 14 | canvas-generate | canvas-generate | Visual canvas | post.canvas |

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Orchestrator Dispatch

For each agent, spawn via Agent tool with this prompt template:

Local Agent Template

You are agent <agent-name> in the KTG Hub-Swarm pipeline.
Your job: call Ollama and produce ONE artifact.

Input file: <post-file-path>
Post title: <title>
Post slug: <slug>

Run this curl command (exact):
<curl-command-from-manifest>

Save stdout to: <output-path>
Report back with: success/failure, output path, and any errors.

Cloud Agent Template

You are agent <agent-name> in the KTG Hub-Swarm pipeline.
Your job: invoke ONE plugin skill and produce ONE artifact.

Input file: <post-file-path>
Post title: <title>
Post slug: <slug>

Use the Skill tool with name="<skill-name>" and pass this exact prompt:
<prompt-from-manifest-below>

Save your output to: <output-path>
Report back with: success/failure, output path, and any errors.

---

Per-Agent Prompts

Repurpose Agents (1–5)

Local (default):

Agent repurpose-medium:

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\nOutput only the article.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-medium.md

Agent repurpose-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 post:\n<post-content-or-summary>\n\nOutput only the post.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-reddit.md

Agent repurpose-x:

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\nOutput only the thread, one tweet per line.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-x-thread.md

Agent repurpose-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 post:\n<post-content-or-summary>\n\nOutput only the post.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-meta.md

Agent repurpose-linkedin:

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\nOutput only the article.","stream":false}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['response'])" > social-linkedin.md

Cloud (--cloud): All use Skill tool with name="blog-repurpose". Prompt:

Read the blog post at <file-path>. Repurpose for ALL platforms: Medium, Reddit, X, Meta, LinkedIn. Generate all five variants. Save to <dir>/social-*.md.

Image Agent (6)

Agent image-hero:

Use Skill tool with name="blog-image". Prompt: "Generate a hero image for a blog post titled ''. Topic: <summary>. Style: editorial, AI/tech theme, no text overlay. Aspect ratio: 16:9. Save to <dir>/hero-16x9.png."

If blog-image MCP unavailable, fall back to Skill tool with name="banana". Prompt: "Generate a hero image for ''. <summary>. Style: editorial, AI/tech. Aspect ratio: 16:9. Size: 2K. Save to <dir>/hero-16x9.png."

After hero generation, run ImageMagick crops (can be a 12th agent or inline):

magick "<dir>/hero-16x9.png" -resize 1200x627^ -gravity Center -extent 1200x627 "<dir>/hero-linkedin.png"
magick "<dir>/hero-16x9.png" -resize 1200x675^ -gravity Center -extent 1200x675 "<dir>/hero-x.png"
magick "<dir>/hero-16x9.png" -resize 1080x1080^ -gravity Center -extent 1080x1080 "<dir>/hero-square.png"

SEO Agents (7–11)

Local (default):

Agent seo-geo:

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\nRate 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

Agent seo-page:

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\nScore 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

Agent seo-technical:

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\nCheck: 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

Agent seo-schema:

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\nRequired: 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

Agent seo-check:

curl -s http://localhost:11434/api/generate \
  -d '{"model":"Qwopus:latest","prompt":"Run post-writing SEO validation checklist.\n<full post>\n\nCheck: 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

Cloud (--cloud): All use respective Skill tools (blog-geo, seo-page, seo-technical, seo-schema, blog-seo-check) with file path prompts.

Optional Ads Agents (12–13)

Agent ads-create (local):

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\nOutput: 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

Agent ads-generate (cloud — image gen):

Use Skill tool with name="ads-generate". Prompt: "Read <dir>/campaign-brief.md and brand-profile.json. Produce platform-sized ad images. Save to <dir>/ad-creative-*.png."

Optional Canvas Agent (14)

Agent canvas-generate:

Use Skill tool with name="canvas-generate". Prompt: "Create a visual canvas from the blog post at <file-path>. Detect best archetype, generate content and visuals, instantiate template, apply layout, produce complete canvas. Save to <dir>/post.canvas."

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Gather Phase

Wait for all agents to return. Collect:

1. Success/failure per agent
2. Output file paths
3. Scores (GEO, SEO, technical, blog-seo-check)
4. Any errors

Assemble artifact manifest. If any agent failed, mark it in the manifest.

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Review Gate

Present the manifest exactly as defined in hub-swarm skill. Wait for explicit YES.

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Publish Phase

Spawn 3 parallel publish agents:

Agent	Task
publish-vercel	Deploy post via Composio, capture canonical URL
publish-wordpress	Publish via wordpress-mcp-ultimate MCP
publish-social	Fire Reddit, LinkedIn, X, Meta via Composio

Each publish agent receives the canonical URL from publish-vercel before firing.

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Notes

• Local-first by default: 10 of 11 core agents use Ollama. Only image-hero uses cloud (blog-image/banana).
• Ollama models: Ministral:latest (13.5B) for content + schema, Qwopus:latest (9B) for SEO/GEO/analysis
• Context limit: ~32k tokens. For posts >8k words, chunk into summaries before Ollama calls.
• Cloud skill invocations use the Skill tool with exact name= matching the skill frontmatter
• Agents do not share context — each loads its own skill/Ollama call independently
• Orchestrator stays under 4k tokens by keeping per-agent prompts minimal
• Never Read skill files directly — invoke via Skill tool or Ollama API only
• Review gate is the only sequential bottleneck (by design)