shape-dataset

name

shape-dataset

description

Main entry point for turning raw data into a dataset. Examines a source (GitHub repo, local path, URL), works with the user to define the target task, then plans and executes a prep pipeline — profiling, PII handling, column curation, format normalization, splits, and annotation schema design. Delegates specialized work to subagents. Use when the user says "prep this data", "turn this into a dataset", "get this ready for annotation", or "build a dataset from X".

Prep Dataset (Orchestrator)

This is the top-level workflow for taking raw data and getting it into shape for a dataset — typically one destined for annotation and publication on Hugging Face. It does not try to enumerate every possible operation as a separate skill. Instead, it inspects the data, understands the user's goal, and decides which preparatory steps to run, delegating each to a specialized subagent.

Inputs to gather

1. Source — a GitHub repo URL, a local path, or a remote URL. If unclear, invoke the ingest-source skill to stage the data first.
2. Target task — what is the dataset for? (e.g. classification, instruction tuning, NER, RAG eval, preference pairs). This drives every downstream decision.
3. Sensitivity expectation — does the data potentially contain PII? Is the published dataset intended to be public?
4. Working directory — where prepared artifacts are written. Default: $CLAUDE_USER_DATA/data-annotation/workspaces/<dataset-name>/ resolved as:

DATA_ROOT="${CLAUDE_USER_DATA:-${XDG_DATA_HOME:-$HOME/.local/share}/claude-plugins}/data-annotation"

If the user wants the workspace under a path they own (e.g. ~/repos/...), store only the pointer to that path in $DATA_ROOT/config.json and write artifacts there.

Workflow

1. Stage the source

If not already on disk, invoke the ingest-source skill. End state: a single directory with the raw data files visible.

2. Profile

Delegate to the data-profiler subagent. Expect back: file inventory, format(s), row/record counts, schema (column names + inferred types), null/dup rates, encoding issues, and a small sample.

3. Surface concerns and propose a plan

Based on the profile and the target task, write a short prep plan to the workspace as prep-plan.md. Typical line items:

• PII: if the profiler flagged free-text columns or obvious identifier columns, queue the pii-scanner subagent.
• Column curation: if there are columns the target task won't use, queue the column-curator subagent.
• Format issues: nested JSON to flatten, encoding to fix, format conversion (e.g. CSV → JSONL/Parquet) — queue the format-normalizer subagent.
• Reshaping: if the target is annotation, the data needs to become one row per annotation task with a stable id — queue format-normalizer for this too.
• Splits: train/val/test if appropriate — queue format-normalizer.
• Annotation schema: if the goal includes annotation, queue the schema-designer subagent after reshape.
• Other: deduplication, language detection, length filtering, toxicity screening — call out only if relevant signals appeared in the profile.

Show the plan to the user. Do not execute steps the user hasn't approved. Let them strike, reorder, or add items.

4. Execute approved steps

Run each approved step by invoking the corresponding subagent with a precise brief: input path, output path, the relevant slice of the profile, and the target task. After each step, re-profile briefly so the next step works on current data.

Persistent intermediate artifacts go in <workspace>/stages/<NN>-<step>/. The final cleaned dataset lands in <workspace>/final/.

5. Hand off

When prep is done, suggest the next skill based on scale and feedback needs:

• Small annotation (≤ ~200 records, schema may still need tuning): annotate-with-claude — Claude annotates each record interactively in-session, with the user supervising.
• Larger annotation (hundreds to thousands, schema locked): scaffold-annotation-env — sets up Gemini batch inference over the prepared data.
• For publication: hf-setup — creates the HF dataset repo, copies data over, generates the card.
• End-to-end: annotate (either skill), then hf-setup once labels are populated.

Operating principles

• Diagnose, don't dictate. Look at the data and the task before recommending operations. Don't run PII redaction on a synthetic benchmark; don't strip columns from an already-clean dataset.
• Confirm before destructive steps. Column drops, redaction, and reshaping change the data. Always show the user what will be removed/transformed before doing it.
• Keep stages. Never modify the raw input in place. Each step produces a new stage directory so the user can audit or roll back.
• Single source of truth for the schema. Once a schema is locked (post-reshape), write it to <workspace>/schema.json and refer back to it from every later stage.