# Automated Tearsheet

> Ingest market data, backtest a supplied strategy definition with Qlib, and render the resulting performance metrics as a formatted tearsheet document. Use when a researcher wants a repeatable, generated performance report from a single strategy backtest; this workflow never places or recommends live trades.

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

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# Automated Tearsheet Workflow

Compose the named atomic skills without adding presentation logic here.

## Inputs

Provide the strategy definition, asset universe, date range, and benchmark.

## Steps

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

Invoke `$quant-data-ingest` with the workflow inputs to ingest market
data for the universe and date range.

Expected: `normalized_market_dataset`

### Step 1: qlib-backtester [skill: qlib-backtester] [depends_on: Step 0]

Invoke `$qlib-backtester` with `normalized_market_dataset` and the
strategy definition.

Expected: `backtest_report`

### Step 2: document-converter [skill: document-converter] [depends_on: Step 1]

Invoke `$document-converter` with `backtest_report` to render it as a
formatted Markdown/PDF tearsheet.

Expected: `tearsheet_document`

## Output

Return `backtest_report` and `tearsheet_document`. Do not place or recommend live
trades.

## Execution

- **Run first:** Step 0 — `$quant-data-ingest`.
- **After level 0:** Step 1 — `$qlib-backtester`.
- **After level 1:** Step 2 — `$document-converter`.

**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.