Esg Screening Pipeline

ESG Screening Pipeline Workflow

Compose the named atomic skills without adding scoring logic here.

Inputs

Provide the company universe and the ESG disclosure categories of interest.

Steps

Step 0: web-search [skill: web-search]

Invoke $web-search with the workflow inputs to locate publicly disclosed ESG reports and filings for the universe.

Expected: esg_source_packet

Step 1: quant-data-ingest [skill: quant-data-ingest] [depends_on: Step 0]

Invoke $quant-data-ingest with esg_source_packet to normalize the disclosed data into the Timeseries Memory backend.

Expected: normalized_esg_dataset

Step 2: data-quality-auditor [skill: data-quality-auditor] [depends_on: Step 1]

Invoke $data-quality-auditor with normalized_esg_dataset and the declared rule set.

Expected: quality_report

Output

Return normalized_esg_dataset and quality_report. Does not compute or assert a proprietary ESG score.

Execution

• Run first: Step 0 — $web-search.
• After level 0: Step 1 — $quant-data-ingest.
• After level 1: Step 2 — $data-quality-auditor.

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.