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embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
numpy-linalg
Imported from https://github.com/benchflow-ai/SkillsBench.
ml-inference-optimization
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
pytorch-distributed
Distributed training strategies including DistributedDataParallel (DDP) and Fully Sharded Data Parallel (FSDP). Covers multi-node setup, checkpointing, and process management using torchrun. (ddp, fsdp, distributeddataparallel, torchrun, nccl, rank, process-group)
mesh-analysis
Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.
time_series_anomaly_detection
Detect anomalies in time series data using Prophet Framework (Meta), which frames the seasonality, trend holiday effect and other needed regressors into its model, to identify unusual surges or slumps in trends. This is a general methodology analyst can use for understanding what changes of their tracking metrics are manifesting anomalies pattern.
python-parallelization
Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.
geospatial-analysis
Analyze geospatial data using geopandas with proper coordinate projections. Use when calculating distances between geographic features, performing spatial filtering, or working with plate boundaries and earthquake data.
sqlite-map-parser
Parse SQLite databases into structured JSON data. Use when exploring unknown database schemas, understanding table relationships, and extracting map data as JSON.
data
Imported from https://github.com/cyberkaida/reverse-engineering-assistant.
prompt-optimizer
Prompt engineering expert that helps users craft optimized prompts using 57 proven frameworks. Use when users want to optimize prompts, improve AI instructions, create better prompts for specific tasks, or need help selecting the best prompt framework for their use case.
backup-library
A Ruby script that creates timestamped ZIP backups of libraries directories, excluding video files. It backs up library.yaml, transcripts, and roughcuts. Provides clear commands for verification and restoration. Useful for project data protection but limited to specific directory structure.
physicist-analyst
Analyzes events through physics lens using fundamental laws (thermodynamics, conservation, relativity), quantitative modeling, systems dynamics, and energy principles to understand causation, constraints, and feasibility. Provides insights on energy systems, physical limits, technological feasibility, and complex systems behavior. Use when: Energy decisions, technology assessment, systems analysis, physical constraints, feasibility evaluation. Evaluates: Energy flows, conservation laws, efficiency limits, physical feasibility, scaling behavior, emergent properties.
outside-in-testing
Generates behavior-driven tests using AI agents to verify application interfaces without internal knowledge, creating platform-agnostic YAML scenarios that survive refactoring and validate user workflows.
custom-distance-metrics
Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
trend-analysis
Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.
vulnerable-secret
Guidance for extracting secrets from protected or obfuscated binaries through systematic static and dynamic analysis. This skill should be used when tasks involve reverse engineering executables, extracting hidden flags or keys, analyzing binary protections, or decoding obfuscated data within compiled programs.
assisting-reverse-engineering
Provides reverse engineering analysis support including function identification, data structure analysis, and behavior understanding. Use when analyzing unknown binaries, understanding program structure, or investigating binary behavior.
validation-scripts
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.
python-analytics
Python data analysis with pandas, numpy, and analytics libraries
retention-analysis
Analyze user retention and churn using survival analysis, cohort analysis, and machine learning. Calculate retention rates, build survival curves, predict churn risk, and generate retention optimization strategies. Use when working with user subscription data, membership information, or when user mentions retention, churn, survival analysis, or customer lifetime value.
history
Git data you can use, not parse. Understand why code exists.
python-json-parsing
Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath). Use when working with JSON data, parsing APIs, handling large JSON files, or optimizing JSON performance.
data_cleaning
Clean messy tabular datasets with deduplication, missing value imputation, outlier handling, and text processing. Use when dealing with dirty data that has duplicates, nulls, or inconsistent formatting.