데이터·AI 스킬

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

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• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

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• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

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• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

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• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Estimate inference memory for models on Hugging Face Hub without downloading their weights. Check GPU capacity for Safetensors or GGUF files, with optional estimates for the KV cache, memory used to retain attention data during generation.

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• Check whether a model fits on a GPU
• Compare memory estimates for GGUF quantizations
• Estimate KV cache memory for a chosen context length

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

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• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

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• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.

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• Preview dataset contents and data splits
• Search text and filter dataset rows
• Inspect dataset sizes, statistics, and Parquet links

Create charts for numeric data and diagrams for processes or relationships. Explains how to choose ECharts or Mermaid, apply clear formatting, and use chart_render to display the result in chat and save a downloadable PNG.

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• Comparing revenue across regions or products
• Showing trends across multiple metrics
• Turning workflows or system relationships into diagrams

Use defined customer and order tables to build sales analysis queries with consistent business rules. Calculate revenue from completed orders, rank customers by spending, and identify high-value orders using the supplied thresholds and SQL example.

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• Ranking the top ten customers by revenue over three months
• Calculating revenue from completed orders
• Finding orders with a total amount above 1,000

Have Claude and GPT critique each other's answers, revise their positions, and work toward a combined response. Choose the number of rounds and see what changed, where they agree, and which disagreements remain.

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• Stress-testing a proposal from two perspectives
• Finding weaknesses in competing answers
• Combining agreement while preserving unresolved differences

Route requests to general assistance, medical source checks, educational answers, structured learning, or registration and subscriptions. It considers the intended use rather than isolated medical words, and refuses high-risk requests such as individual diagnosis or prescription dosing.

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• Separating general tasks from medical learning requests
• Routing document authority and version checks
• Choosing a workflow for medical explanations or structured study

Retrieve financial data for Chinese markets and selected global markets using akshare-cli. Find stock prices, fund values, exchange rates, economic indicators, and financial news, then extract recent records or save results in common file formats.

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• Retrieve historical prices for Chinese, Hong Kong, or US stocks
• Check fund and ETF net asset values
• Look up exchange rates and economic indicators

Prepare an AI-SPEC.md design agreement for an AI development phase. The skill coordinates framework selection, documentation and domain research, and evaluation planning so the phase has a defined technical direction and assessment strategy.

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• Define the design agreement for an AI development phase
• Research frameworks, documentation, and the application domain
• Plan how to evaluate an AI system

Turn sales, customer, finance, or operations data into an Excel dashboard with clear metric definitions, targets, actual results, achievement rates, and trend charts. Review strong and weak performance alongside unusual changes over time.

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• Build a monthly dashboard from operating data
• Agree on metric definitions and calculation rules
• Track targets, actual results, and achievement rates

Turn raw data into an Excel workbook with cleaned records, formula-based summaries, and charts. Get three to five plain-language conclusions grounded in figures, with possible explanations or recommendations and clear acknowledgment when the data is insufficient.

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• Cleaning inconsistent units and date formats
• Comparing results across periods or years
• Visualizing trends and composition

Turn historical sales data into an Excel forecast with trends, seasonal patterns, uncertainty bands, and optimistic, base, and pessimistic scenarios. Adjustable assumptions and sensitivity tables show how changes in key inputs affect annual totals.

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• Forecasting next year's sales from monthly history
• Comparing optimistic, base, and pessimistic scenarios
• Checking how assumptions affect annual sales

Clean raw survey responses and produce an Excel analysis workbook plus a Word findings report. Summarize each question, compare respondent groups, chart key results, and organize open-ended answers into themes while documenting exclusions and sample limitations.

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• Analyzing survey responses exported as CSV
• Comparing satisfaction results across respondent groups
• Grouping open-ended feedback into themes

Clean customer lists and combine inconsistent tables using agreed rules for duplicates, dates, numbers, and text. Receive an Excel workbook with the original data, cleaned results, cleaning rules, and a record of changes for review.

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• Removing duplicates from a customer list
• Combining tables with inconsistent formats
• Standardizing dates, numbers, and text

Analyze rejected plans and reviewer feedback to identify recurring concerns, changing expectations, and concrete prompt improvements. Produce an HTML dashboard from Plannotator archives, or use Claude Code plan rejection reasons when those archives are unavailable.

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• Finding recurring reasons for plan rejection
• Understanding how review expectations changed
• Deriving planning instructions from past feedback

Build evaluation suites for AI features with the Axiom AI SDK. Generate test files, scoring functions, test cases, and configurable parameters from descriptions and existing code to compare changes and monitor output quality in production.

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• Add regression evaluations for an AI feature
• Score classification, retrieval, and structured outputs
• Compare model and parameter configurations

Translates Splunk SPL queries into Axiom APL, the query language used to search and analyze data in Axiom. Covers command and function equivalents, time filters, type conversions, and common patterns for migration or learning.

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• Migrating Splunk queries to Axiom
• Converting log aggregation and error-rate queries
• Learning APL through familiar SPL patterns

Add an AI agent backend to a Convex app with persistent conversations, streamed messages, and tool calls. It also supports document retrieval for grounded answers, using Convex AI Gateway for model calls where available.

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• Add an AI conversation feature to a Convex app
• Store conversation threads and message history
• Build answers using retrieved documents