research/optimal-schedule

Optimal Schedule — LLM-driven posting schedule from trend snapshots.

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

Input Schema

Field	Type	Default	Description
currentSlots	string	"[]"	JSON array of existing schedule slots [{provider, dow, hour}]
niche	string	required	Creator’s content niche
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.
targetAudience	string	""	Target audience description
timezone	string	"UTC"	Creator’s timezone (IANA, e.g. America/New_York)
trends	string	required	JSON array of trend objects — each with topic, source, score, velocity, relevance, opportunity, summary
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

validate_inputs → compute_schedule

Task	Description
validate_inputs	Parse JSON strings, validate, and prepare context for the LLM task.
compute_schedule	Use LLM to determine optimal posting times per platform.

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/optimal-schedule

# Optional fields — copy any line(s) under `input:` and uncomment to set:
# JSON array of existing schedule slots [{provider, dow, hour}]
#   currentSlots: "[]"
#
# Target audience description
#   targetAudience: ""
#
# Creator's timezone (IANA, e.g. America/New_York)
#   timezone: UTC
#

input:
  # Creator's content niche
  niche: ""

  # JSON array of trend objects — each with topic, source, score, velocity, relevance, opportunity, summary
  trends: ""

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/optimal-schedule' \
  -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 compute_schedule has no Pydantic return type — workflow output schema is null. Declare a WorkflowOutput subclass and pass it to Flow(output=…) for a strict contract.