# Dataset Readiness Assessment

> Profile a dataset, validate it against reviewed quality rules, and produce an owner-confirmed data dictionary. Use when a local CSV, TSV, JSON, or JSON Lines dataset must be assessed before analysis, ingestion, governance review, or handoff.

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

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# Dataset Readiness Assessment Workflow

Profile a dataset, validate it against reviewed quality rules, and produce an owner-confirmed data dictionary. Use when a local CSV, TSV, JSON, or JSON Lines dataset must be assessed before analysis, ingestion, governance review, or handoff.

## Steps

### Step 1: dataset-profiler [depends_on: none]
**Agent**: `profiling-specialist`
**Skill**: `dataset-profiler`

### Step 2: data-quality-auditor [depends_on: dataset-profiler]
**Agent**: `quality-specialist`
**Skill**: `data-quality-auditor`

### Step 3: data-dictionary-builder [depends_on: dataset-profiler]
**Agent**: `documentation-specialist`
**Skill**: `data-dictionary-builder`

## Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.

- **Run first (in parallel):** Step 1 — dataset-profiler
- **After level 0:** Step 2 — data-quality-auditor; Step 3 — data-dictionary-builder

**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.