# Earnings Calendar Pipeline

> Locate publicly reported earnings-announcement dates for a supplied ticker universe and ingest them into the Timeseries Memory backend. Use when a researcher needs a structured earnings calendar as input to another pipeline; this workflow does not forecast earnings surprises or trade around them.

- Skill: `knuckles-team/earnings-calendar-pipeline` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add knuckles-team/earnings-calendar-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knuckles-team/earnings-calendar-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: MIT
- Author: Knuckles-Team (https://skillmd.com/u/knuckles-team)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/knuckles-team/earnings-calendar-pipeline

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# Earnings Calendar Pipeline Workflow

Compose the named atomic skills without adding forecasting logic here.

## Inputs

Provide the ticker universe and the lookahead window.

## Steps

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

Invoke `$web-search` with the workflow inputs to locate publicly
reported earnings-announcement dates for the universe.

Expected: `earnings_date_source_packet`

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

Invoke `$quant-data-ingest` with `earnings_date_source_packet` to
normalize the dates into the Timeseries Memory backend.

Expected: `normalized_earnings_calendar`

## Output

Return `normalized_earnings_calendar`. Does not forecast surprises or trade
around the dates.

## Execution

- **Run first:** Step 0 — `$web-search`.
- **After level 0:** Step 1 — `$quant-data-ingest`.

**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.