data-annotation

Plugin data-annotation in danielrosehill/Claude-Data-Annotation-Plugin. Commands read from the repo's manifest — 23 days ago.

Skills (7)

• annotate-with-claude

  Annotate a small dataset directly inside the Claude Code session — Claude reads each record, applies the locked schema, and writes structured annotations to disk. For datasets where standing up Label Studio or a Gemini batch job would be overkill (typically tens to low hundreds of records). Use when

• hf-annotation-space

  Design and stand up a private Hugging Face Space for humans to review, correct, label or browse a dataset. Each job gets a UI designed for its reviewer action (edit in place with reasons, judge accept/reject, constrained edit, …), starting from the closest template in a small library rather than one

• hf-setup

  Set up a Hugging Face dataset repository — create the remote repo (asking public/private), copy prepared data over, generate a dataset card, and push. Uses the huggingface-cli, not an MCP. Use when the user says "set up a HF dataset", "publish to Hugging Face", "create the HF dataset repo", or after

• ingest-source

  Stage raw data from a GitHub repo, local path, or remote URL into a working directory so downstream prep skills can operate on it. Use when the user provides a data source and wants it pulled in, or when `shape-dataset` needs to ingest before profiling.

• init-dataset-repo

  Initialize a local git repository for a dataset, with the conventional Hugging Face layout, LFS configuration, license, and a stub dataset card. Use when the user wants to start a new dataset repo, or as a step inside `hf-setup`.

• scaffold-annotation-env

  Create a self-contained annotation workspace for a prepared dataset, including a locked annotation schema and ready-to-run boilerplate for batch inference via the Gemini API. Use when the user says "set up annotation", "create annotation environment", "scaffold annotation", or after `shape-dataset`

• shape-dataset

  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. Deleg

Subagents (6)

• column-curator

  Recommend which columns to keep, drop, rename, or transform given the dataset profile and the target task. Produces a curation plan; applies it only on instruction.

• data-profiler

  Profile a staged dataset — file inventory, format detection, schema inference, row/record counts, null and duplicate stats, encoding issues, and representative samples. Read-only. Invoked by `shape-dataset` early in the pipeline.

• format-normalizer

  Execute format conversions, encoding fixes, JSON flattening, reshaping into one-record-per-task, and train/val/test splits. Operates on approved plans from the orchestrator; writes outputs to a new stage directory.

• pii-scanner

  Scan a dataset for personally identifiable information and propose a redaction strategy per column. Detects emails, phone numbers, names, addresses, government IDs, IP addresses, credit cards, and free-text leaks. Produces a redaction plan; only redacts when explicitly told to.

• review-annotations

  Audit a set of completed annotations — schema validation, label distribution, sampled spot-checks, agreement analysis if multiple passes exist, and a list of records flagged for human review. Produces an audit report; does not modify annotations.

• schema-designer

  Propose an annotation schema (label set, field definitions, guidelines, edge cases) for a dataset given a target task and a profile. Produces schema.json plus human-readable guidelines. Iterates with the user before the schema is locked.

Similar plugins