adr-creator
Create Architecture Decision Records (ADRs) documenting significant technical decisions for the FF Analytics platform. This skill should be used when making architectural choices, evaluating alternatives for data models or infrastructure, documenting trade-offs, or when the user asks "should we use X or Y approach?" Guides through the ADR creation workflow from context gathering to documentation.
data-ingestion-builder
Build new data ingestion providers following the FF Analytics registry pattern. This skill should be used when adding new data sources (APIs, files, databases) to the data pipeline. Guides through creating provider packages, registry mappings, loader functions, storage integration, primary key tests, and sampling tools following established patterns.
data-quality-test-generator
Generate comprehensive dbt test suites following FF Analytics data quality standards and dbt 1.10+ syntax. This skill should be used when creating tests for new dbt models, adding tests to existing models, standardizing test coverage, or implementing data quality gates. Covers grain uniqueness, FK relationships, enum validation, and freshness tests.
dbt-model-builder
Create dbt models following FF Analytics Kimball patterns and 2×2 stat model. This skill should be used when creating staging models, core facts/dimensions, or analytical marts. Guides through model creation with proper grain, tests, External Parquet configuration, and per-model YAML documentation using dbt 1.10+ syntax.
notebook-creator
Create Jupyter notebooks for FF Analytics following project conventions. Use this skill when the user requests analysis notebooks for player evaluation, roster health, trade scenarios, market trends, or projection quality analysis. Guides through notebook structure, DuckDB connections, mart queries, visualization standards, and freshness banner patterns.
sprint-1-executor
Execute Sprint 1 tasks for FASA optimization and trade analytics. This skill should be used when the user requests execution of any Sprint 1 task (Tasks 1.1-1.4, 11-13, 2.1-2.4, 3.1-3.2), including cap space parsing, Sleeper integration, FASA target marts, FA acquisition history, roster depth analysis, enhanced FASA targets, notebooks, valuation models, trade analysis, automation workflows, or documentation. Each task is atomic, standalone, and designed for independent execution with built-in validation. Current focus: Phase 2 FASA Intelligence (Tasks 11-13, 1.4).
sprint-planner
Design and structure development sprints with atomic, LLM-ready task files. This skill should be used when the user wants to plan a new development sprint, break down complex projects into executable tasks, or create a sprint execution framework. Produces sprint plans, atomic task specifications, and corresponding executor skills for LLM coding agents.
strategic-planner
Design comprehensive technical specifications and strategic plans for data architecture and analytics projects. This skill should be used when planning major features, creating SPEC documents, assessing product requirements, breaking down complex projects into phases, or documenting architectural strategies like SPEC-1. Guides through requirements gathering, MoSCoW prioritization, phase planning, and open items tracking.
ff-dynasty-strategy
Expert guidance on dynasty fantasy football strategies, player valuation frameworks, roster construction, trade evaluation, and asset management. Use this skill when analyzing dynasty trades, evaluating player value, designing roster strategies, assessing competitive windows, or answering fantasy football domain questions. Covers VoR/VBD methodologies, aging curves, market inefficiencies, draft pick valuation, and win-now vs rebuild strategies.
ff-ml-modeling
Expert guidance on machine learning and feature engineering for fantasy football player projection models. Use this skill when building predictive models, engineering features from player statistics, selecting appropriate ML algorithms, or addressing sports-specific ML challenges. Covers feature engineering patterns, model selection frameworks, validation strategies, and interpretability techniques for fantasy football analytics.
ff-statistical-methods
Expert guidance on statistical analysis methodologies and Monte Carlo simulation for fantasy football. Use this skill when selecting regression approaches, designing simulations, performing variance analysis, or conducting hypothesis tests. Covers regression types (OLS, Ridge, Lasso, GAMs), Monte Carlo frameworks, regression-to-mean analysis, and statistical best practices for player performance modeling.