dataset-publishing

Complete reference for model upload and deployment. Covers HuggingFace upload, save strategies (LoRA, merged 16-bit, merged 4-bit), GGUF conversion, model merging, model cards, and the full upload workflow. Use when uploading models, creating GGUF files, merging LoRA adapters, or deploying to HuggingFace. This skill is about USING the upload/deployment tools via CLI — never modifying source code.

Complete reference for the SynthChat synthetic dataset generation system. Covers CLI commands (generate, improve, validate), scenario YAML authoring, rubric YAML authoring, settings configuration, evaluation, and full workflow. Use when generating datasets, writing rubrics/scenarios, configuring models/workers, improving dataset quality, or running evaluations. This skill is about USING the system via CLI and YAML — never modifying source code.

Create structured research notes from experiment runs and analysis artifacts. Use when creating a note at run launch, updating it as training/evaluation/loss stages finish, summarizing a finished run, comparing experiment outcomes, extracting hypotheses from eval/loss artifacts, or proposing next-run actions grounded in `.tracking/experiments/<id>/analysis/` outputs. This skill is about turning repo-native experiment evidence into stable, machine-readable markdown.

Complete reference for the fine-tuning pipeline (SFT, KTO, GRPO), cloud HF Jobs workflows, autonomous experiment search, checkpoint evaluation, and LoRA surgery. Covers training CLI flags, YAML configuration, model presets, dataset requirements, LoRA settings, training monitoring, hyperparameter search, and post-training optimization. Use when training models, configuring training runs, choosing hyperparameters, running cloud experiments, inspecting HF jobs, or troubleshooting training issues. This skill is about USING the training system via CLI and YAML — never modifying source code.

Complete reference for the config-first model evaluation system. Covers the Evaluator CLI, assertion-driven YAML scenarios, response views, backend configuration, presets, scoring, LLM-as-judge, model comparison, and HuggingFace integration. Use when evaluating models, writing test prompts, comparing training runs, or interpreting eval results. This skill is about USING the evaluation system via CLI and YAML.

End-to-end case studies showing how to implement the full training pipeline for different skill types. Covers three complete worked examples — tool-calling training, essay-style training, and agentic search (RAG agent) training — demonstrating dataset design, synthetic generation, validation, fine-tuning, evaluation, and iteration. Use when onboarding to the project, understanding how all components fit together, explaining the pipeline to others, or planning a new training capability. This skill is about UNDERSTANDING the system holistically — reference the other skills for specific CLI commands.

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.

INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.

Use this skill to fully reproduce and operate a local end-to-end WeChat Official Account publishing workflow: prepare the environment, validate dependencies, configure non-sensitive placeholders for credentials, gather source material, draft articles, prepare cover and body images, assemble a WeChat-ready Markdown package, publish to draft, optionally submit for formal publication, poll status, archive outputs, and attach scheduling or alerting. Use whenever the user wants a complete reproducible公众号自动发文 skill with environment setup, templates, runbooks, and execution scaffolding, while keeping all secrets and personal account details outside the skill package. Key real-world findings: freepublish does not always behave like manual platform publishing for homepage visibility, production mode should often default to draft-only, image files must be validated by real format rather than extension alone, and multi-account deployments should use isolated directories.

A specialized auditor for Reddit Devvit developers to verify app readiness before uploading to the Reddit servers. It ensures compliance with Devvit CLI v0.12.x and Reddit’s publishing standards.

Use this skill when users need to search for datasets, download data files, or explore data repositories. Triggers include: requests to "find datasets", "search for data", "download dataset from Kaggle", "get data from Hugging Face", "find ML datasets", or mentions of data repositories like Kaggle, UCI ML Repository, Data.gov, or Hugging Face. Also use for previewing dataset statistics, generating data cards, or discovering datasets for machine learning projects. Requires OpenClawCLI installation from clawhub.ai.