Knuckles-Team Market Data Ingestion Workflow
Compose the named atomic skills without adding backtest logic here.
Inputs
Provide the asset universe, date range, and data-quality rule set.
Steps
Step 0: quant-data-ingest [skill: quant-data-ingest]
Invoke $quant-data-ingest with the workflow inputs to ingest market
data into the Timeseries Memory backend.
Expected: normalized_market_dataset
Step 1: data-quality-auditor [skill: data-quality-auditor] [depends_on: Step 0]
Invoke $data-quality-auditor with normalized_market_dataset and
the declared rule set.
Expected: quality_report
Step 2: dataset-profiler [skill: dataset-profiler] [depends_on: Step 1]
Invoke $dataset-profiler with normalized_market_dataset to produce
a structural and statistical profile.
Expected: dataset_profile
Output
Return normalized_market_dataset, quality_report, and dataset_profile. Does
not backtest or place trades.
Execution
- Run first: Step 0 —
$quant-data-ingest. - After level 0: Step 1 —
$data-quality-auditor. - After level 1: Step 2 —
$dataset-profiler.
Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.