Loading...
Loading...
Found 1,653 Skills
Find incomplete records, normalize field values in bulk, dedupe with `hubspot objects merge`, and audit custom properties. Builds on `bulk-operations` for JSONL piping and dry-run/digest/confirm.
Use when composing iii primitives into backend architectures: durable workflows, reactive backends, agentic pipelines, event-driven CQRS, effect pipelines, and trigger-transform-action automation.
End-to-end ETL pipeline for Harvard Art Museums API with SQL analytics and Streamlit visualization
10 document processing skills. Trigger: extracting text from PDFs, parsing references, document Q&A. Design: parsing pipelines (GROBID, marker) and structured extraction tools.
Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN/RNN/MLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD/ATC/NDC/RxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD/ATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if "PyHealth" isn't named explicitly.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
Debug NestJS issues systematically. Use when encountering dependency injection errors like "Nest can't resolve dependencies", module import issues, circular dependencies between services or modules, guard and interceptor problems, decorator configuration issues, microservice communication errors, WebSocket gateway failures, pipe validation errors, or any NestJS-specific runtime issues requiring diagnosis.
Debug Rails issues systematically. Use when encountering ActiveRecord errors like RecordNotFound, routing issues, N+1 query problems detected by Bullet, asset pipeline issues, migration failures, gem conflicts, ActionController errors, CSRF token problems, or any Ruby on Rails application errors requiring diagnosis.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
Refactor NestJS/TypeScript code to improve maintainability, readability, and adherence to best practices. Identifies and fixes circular dependencies, god object services, fat controllers with business logic, deep nesting, and SRP violations. Applies NestJS patterns including proper module organization, provider scopes, custom decorators, guards, interceptors, pipes, DTOs with class-validator, exception filters, CQRS, repository pattern, and event-driven architecture. Transforms code into exemplary implementations following SOLID principles.