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claude
Claude Data Annotation
End-to-end data annotation toolkit. Prep raw data, design annotation schemas, annotate interactively with Claude (small scale) or scaffold Gemini batch inference (large scale), and publish to Hugging Face.
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Plugin data-annotation from marketplace danielrosehill in danielrosehill/Claude-Code-Plugins. Commands read from the repo's manifest — today.
What it installs
Skills (7)
annotate-with-claudeAnnotate 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-spaceDesign 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-setupSet 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-sourceStage 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-repoInitialize 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-envCreate 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-datasetMain 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-curatorRecommend 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-profilerProfile 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-normalizerExecute 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-scannerScan 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-annotationsAudit 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-designerPropose 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.
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claude & codex plugins185 plugins