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Multi Dataset Unification Pipeline

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multi-dataset-unification-pipeline

Multi Dataset Unification Pipeline is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

When the user requests to find, analyze, and merge multiple datasets from Hugging Face or similar repositories into a unified format. This skill handles the complete pipeline searching for datasets, examining their structure, parsing format specifications, converting diverse dataset formats (ToolACE, Glaive, XLAM, etc.) into a standardized schema, and merging them into a single output file. Key triggers include requests involving 'dataset unification', 'format conversion', 'merge datasets', 'Hugging Face datasets', 'tool-calling datasets', 'JSONL output', or when users provide a format specification document like 'unified_format.md'.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/multi-dataset-unification-pipeline-zjunlp-skillnet/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/multi-dataset-unification-pipeline, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Instructions

1. Understand the Request

  • Identify the target datasets (names, IDs, or search queries).
  • Locate and read any provided format specification (e.g., unified_format.md).
  • Clarify the scope: number of entries per dataset, output file name/location.

2. Search and Validate Datasets

  • Use huggingface-dataset_search to find each dataset.
  • Use huggingface-hub_repo_details to get metadata and confirm availability.
  • Note the dataset size and structure.

3. Analyze Dataset Structures

  • Load a sample from each dataset using datasets.load_dataset.
  • Examine the column names and a few sample entries.
  • Identify the native format (e.g., ToolACE uses system/conversations, Glaive uses conversations/tools, XLAM uses query/answers/tools).

4. Convert to Unified Format

  • Always refer to the provided unified_format.md (or equivalent) for the target schema.
  • Key Rules:
    • conversation_id: Format as {source_short_name}_{index} (e.g., toolace_0).
    • messages: List of messages with role (user/assistant/tool), content, and optionally tool_calls or tool_call_id.
    • tool_calls: For assistant messages, include id (format tool_call_{n}), name, arguments.
    • tools: List of normalized tool definitions.
    • Remove system messages if they only contain tool instructions.
    • If assistant only made tool calls, set content to null.
    • Do not add tool return results or assistant replies not in the original data.
  • Use the bundled scripts/convert_and_merge.py for reliable, deterministic conversion of ToolACE, Glaive, and XLAM formats.
  • For new dataset formats, write a custom converter following the patterns in the script.

5. Merge and Output

  • Limit entries per dataset as requested (default: first 500).
  • Merge all converted entries into a single list.
  • Write to a JSONL file in the workspace (default: unified_tool_call.jsonl).
  • Verify the output: count entries, check file size, validate format against the specification.

6. Finalize

  • Provide a summary: datasets found, entries converted, output location.
  • Optionally, show a sample entry from each source for validation.
  • Confirm the task is complete.