research/trend-analyst

Trend Analyst — cross-platform social media trend research with scoring.

Category: research
Source: workflows/research/trend_analyst.py

Input Schema

Field	Type	Default	Description
content_pillars	string	""	Comma-separated content pillars
niche	string	required	The niche or industry to analyze
platforms	string	"x,instagram,tiktok,linkedin,youtube"	Comma-separated platforms to analyze (x, instagram, tiktok, linkedin, youtube, reddit)
regenerate	object	—	When set, this run is a regeneration. Workflows may read direction / keep / extra_instructions to modulate prompts; the engine persists parent_run_id and parent_variant_index as run lineage columns.
target_audience	string	""	Who the content is for
trends_per_platform	integer	5	Number of trends to return per platform (3-5)
variants	integer	1	Number of independent variant executions (1–10). When > 1, the engine runs the workflow N times with different sampling, producing N outputs.

Output Schema

No schema defined.

Task Pipeline

plan_trend_research → ingest_x_trends → ingest_instagram_trends → ingest_tiktok_trends → ingest_linkedin_trends → ingest_youtube_trends → ingest_reddit_trends → merge_trend_sources → score_trends → format_trend_output

Task	Description
plan_trend_research	Parse platforms, validate, and build trend-focused queries per platform.
ingest_x_trends	Ingest trending content from X/Twitter.
ingest_instagram_trends	Ingest trending content from Instagram.
ingest_tiktok_trends	Ingest trending content from TikTok via yt-dlp or web search fallback.
ingest_linkedin_trends	Ingest trending content from LinkedIn via web search.
ingest_youtube_trends	Ingest trending YouTube content via yt-dlp (metadata + comments).
ingest_reddit_trends	Ingest trending Reddit content via subreddit discovery + crawling.
merge_trend_sources	Join all platform branches into a unified trend_docs list.
score_trends	Identify and score trending topics from collected platform data.
format_trend_output	Reshape scored trends into the final output schema, grouped by source.

Run-spec example

Save the YAML below as my-run.yaml, edit the values, and run with the CLI or POST it to the API. Required fields are uncommented; optional knobs are documented above the input: block — copy any line under input: and uncomment to set.

workflow: research/trend-analyst

# Optional fields — copy any line(s) under `input:` and uncomment to set:
# Comma-separated content pillars
#   content_pillars: ""
#
# Comma-separated platforms to analyze (x, instagram, tiktok, linkedin, youtube, reddit)
#   platforms: "x,instagram,tiktok,linkedin,youtube"
#
# Who the content is for
#   target_audience: ""
#
# Number of trends to return per platform (3-5)
#   trends_per_platform: 5
#

input:
  # The niche or industry to analyze
  niche: ""

Run it locally:

fab-workflow --from-file my-run.yaml

Or submit over the wire — the same file is the request body:

curl -X POST 'https://gofabric.dev/v1/workflows/runs?name=research/trend-analyst' \
  -H 'Authorization: Bearer fab_xxx' \
  -H 'content-type: application/yaml' \
  --data-binary @my-run.yaml

Every workflow also accepts the universal WorkflowInput fields — variants (1–10 fan-out) and regenerate (creative-direction hints with run lineage). See Run-specs (YAML / TOML / JSON) for the full top-level shape (metadata, priority, bundle, parent, etc.).

Warnings

• Last user task format_trend_output has no Pydantic return type — workflow output schema is null. Declare a WorkflowOutput subclass and pass it to Flow(output=…) for a strict contract.
• Task merge_trend_sources has no Pydantic types — contract is opaque to consumers.