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Found 328 Skills
Quick BI-SmartQ skill with multiple data analysis capabilities: 1. **File Q&A**: Upload Excel/CSV files for intelligent analysis via Quick BI API 2. **Dataset Q&A**: Natural language queries on Quick BI platform datasets, with automatic intelligent table selection and matching 3. **Document Parsing**: Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel 4. **Dashboard Skill Generation**: Auto-convert QuickBI dashboards into data query skills 5. **Data Insight**: Deep data insight analysis on Quick BI datasets 6. **Data Report**: Auto-generate professional data reports based on analysis results Use when users mention data analysis, smart Q&A, querying data, file analysis, document parsing, dashboard skills, data insight, or data reports.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
Generates comprehensive synthetic fine-tuning datasets in ChatML format (JSONL) for use with Unsloth, Axolotl, and similar training frameworks. Gathers requirements, creates datasets with diverse examples, validates quality, and provides framework integration guidance.
Deep research specialist for finding GitHub repos, tools, AI models, APIs, and real data sources. Searches repositories, compares libraries, researches latest AI benchmarks, discovers APIs, locates datasets, and performs competitive analysis to accelerate development.
Writes Pest feature tests for Laravel HTTP controllers using repeatable controller-test patterns across web/session and API/JSON flows. Activates when creating or updating controller tests, nested resource route tests at any depth, CRUD action tests (create, destroy, edit, index, show, store, update), authorization and route-binding scope checks, validation datasets, transport-specific response assertions, and database persistence assertions.
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
Use Fabric CLI for Power BI operations — semantic models, reports, DAX queries, refresh, gateways. Activate when users work with Power BI items, need to refresh datasets, execute DAX, manage reports, or troubleshoot refresh failures.