Marketplace
Find the right skill for the job.
Browse the full catalog through outcome-first channels, technical facets, rating filters, and server-side pagination built for a large public marketplace.
gamma-phase-associator
An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.
numpy-ufuncs
Imported from https://github.com/benchflow-ai/SkillsBench.
CORE
This skill establishes core AI assistant principles including identity, personality traits, and operating workflows. It auto-loads at session start to ensure consistent, efficient, and honest interactions while prioritizing CLI tools and structured communication.
data-wrangler
Transform and export data using DuckDB SQL. Read CSV/Parquet/JSON/Excel/databases, apply SQL transformations (joins, aggregations, PIVOT/UNPIVOT, sampling), and optionally write results to files. Use when the user wants to: (1) Clean, filter, or transform data, (2) Join multiple data sources, (3) Convert between formats (CSV→Parquet, etc.), (4) Create partitioned datasets, (5) Sample large datasets, (6) Export query results. Prefer this over in-context reasoning for datasets with thousands of rows or complex transformations.
sqlite-db-truncate
Guidance for recovering data from corrupted or truncated SQLite database files through binary analysis and manual parsing. This skill applies when working with damaged SQLite databases that cannot be opened with standard tools, particularly when corruption is due to binary truncation, incomplete writes, or filesystem errors.
seisbench-model-api
An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.
caffe-cifar-10
Guidance for building Caffe from source and training CIFAR-10 models. This skill applies when tasks involve compiling Caffe deep learning framework, configuring Makefile.config, preparing CIFAR-10 dataset, or training CNN models with Caffe solvers. Use for legacy ML framework installation, LMDB dataset preparation, and CPU-only deep learning training tasks.
first-order-model-fitting
Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters.
multi-source-data-merger
This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.
mock-data
Generates realistic mock data for testing and development using Faker.js, factories, and fixtures to seed databases, create test cases, and mock API responses efficiently.
api-agent-development-skill
Imported from https://github.com/GolferGeek/orchestrator-ai.
moai-domain-ml
Provides ML development templates for TensorFlow 2.20.0 and PyTorch 2.9.0 with scikit-learn 1.7.2 integration. Includes data processing pipelines, experiment tracking with MLflow, and deployment examples for ONNX and serving frameworks.
mteb-leaderboard
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
api-filtering-sorting
This skill enables secure and flexible API filtering and sorting by parsing query parameters, validating inputs, and preventing common vulnerabilities. It's ideal for building search endpoints, data grids, and dynamic query APIs with support for multiple operators.
database-conventions
This skill enforces Grey Haven's database conventions for multi-tenant applications, including snake_case field naming, mandatory tenant_id for data isolation, standard timestamps, proper indexing, and Row Level Security (RLS) setup for Drizzle (TypeScript) and SQLModel (Python).
ai-daily-news
Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.
smart-recommendations
An AI-powered recommendation engine that analyzes your life patterns to optimize schedules, prioritize tasks, manage energy, and protect focus time for improved productivity and goal achievement.
code-review
Enforces strict code review protocols for AI-assisted development, banning performative responses and requiring evidence before completion claims. Focuses on technical rigor over social comfort with clear triggers for receiving feedback, requesting reviews, and verification gates.
AgentDB Advanced Features
This skill teaches advanced AgentDB features for distributed AI systems, including QUIC synchronization for sub-millisecond node communication, multiple distance metrics, hybrid vector+metadata search, and multi-database management. It provides concrete code examples for each feature.
trader-hand-skill
Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial data sources
cli-agent-starter-kit
Imported from https://github.com/Arize-ai/phoenix.
cohort-analysis
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
customer-success-manager
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.
rag-architect
RAG Architect - POWERFUL