Composed Workflows

Composed workflows chain simpler workflows into end-to-end pipelines. They combine research, hook generation, and video production into single invocations.

Research to Shorts

Workflow: video/research_to_shorts

Deep research on a topic, generate viral hooks from findings, select the best hook, and produce a complete AI short video — grounded in real data.

fabric run video/research_to_shorts \
  --input query="Why developers are switching to Rust" \
  --input platform="TikTok" \
  --input quality=premium

Pipeline

deep_research → bridge_research_to_hooks → generate_hooks →
select_best_hook → generate_script (research-grounded) →
video_production_pipeline

Input

Parameter	Type	Default	Description
query	string	required	Research topic
platform	string	"TikTok"	Target platform
duration_secs	int	45	Video duration
mood	string	"high-energy"	Visual and narrative mood
quality	string	""	Quality preset

Output

Complete video with research-informed script and all intermediate artifacts (research synthesis, hooks, script, b-roll paths).

---

Hot Topics to Short

Workflow: composed/hot_topics_to_short

Scan trending topics, generate hooks, pick the strongest, and produce a single viral short video.

fabric run composed/hot_topics_to_short \
  --input platform="YouTube Shorts" \
  --input categories='["tech", "AI"]'

Pipeline

hot_topics_pipeline → bridge_topics_to_hooks → generate_hooks →
select_best_hook → generate_script → video_production_pipeline

---

Hot Topics to Content

Workflow: composed/hot_topics_to_content

Scan trending topics, generate hooks, and batch-produce content items (scripts, outlines, or posts).

fabric run composed/hot_topics_to_content \
  --input categories='["tech", "business"]' \
  --input num_content=5

Pipeline

hot_topics_pipeline → bridge_topics_to_hooks → generate_hooks →
generate_content_batch

Output

{
  "content_items": [
    {
      "topic": "...",
      "hook": "...",
      "hook_type": "controversial_take",
      "estimated_strength": 0.87
    }
  ]
}

---

Research to Video (Fan-Out)

Workflow: composed/research_to_video

Research a topic, generate hooks, then fan out N parallel AI Shorts runs — one per hook. Produces multiple videos from a single research session.

fabric run composed/research_to_video \
  --input topic="AI replacing junior developers" \
  --input videos=3 \
  --input quality=premium

Pipeline

deep_research → generate_hooks → select_top_N →
fork(ai_shorts × N)

Input

Parameter	Type	Default	Description
topic	string	required	Research topic
videos	int	3	Number of videos to produce
quality	string	""	Quality preset for all videos
--from-research	string	—	Reuse prior research output file
--from-hooks	string	—	Reuse prior hooks output file

---

Research to Hooks

Workflow: composed/my_research_hooks

Lightweight pipeline: research a topic and extract viral hooks. No video production.

fabric run composed/my_research_hooks --input query="AI agents in production"

Pipeline

deep_research → bridge_to_hooks → generate_hooks

Output

{
  "hook_ideas": [
    {
      "hook_text": "Most AI agents fail in production. Here's why.",
      "hook_type": "controversial_take",
      "emotional_trigger": "fear_of_failure",
      "estimated_strength": 0.91
    }
  ]
}

---

Video to Shorts

Workflow: video/video_to_shorts

Extract viral clips from a longer video (podcast, talk, lecture) and produce short-form content.

fabric run video/video_to_shorts \
  --input url="https://youtube.com/watch?v=..."

Pipeline

transcription → detect_viral_moments → extract_clips →
reframe_to_vertical → burn_subtitles → compose_final

---

YouTube Studio

Workflow: youtube/studio

Complete YouTube metadata preparation: transcribe, generate title options, create thumbnails, and write description with chapters.

fabric run youtube/studio --input url="https://youtube.com/watch?v=..."

Pipeline

transcription → title_generation → thumbnail_generation →
description_generation → collect_output

Output

{
  "title": "Selected title",
  "title_options": [...],
  "thumbnail": "/tmp/thumbnail.png",
  "description": "Full YouTube description with chapters...",
  "transcript": [...],
  "duration": 345.6
}