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.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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.
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
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."
Gather Phase
Wait for all agents to return. Collect:
Success/failure per agent
Output file paths
Scores (GEO, SEO, technical, blog-seo-check)
Any errors
Assemble artifact manifest. If any agent failed, mark it in the manifest.
Review Gate
Present the manifest exactly as defined in hub-swarm skill. Wait for explicit YES.
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.
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)
