productivity▌

Scaffold a new built-in enricher for evlog with source, tests, and documentation across six mandatory touchpoints. \n \n Enrichers follow a strict architecture: info interface, factory function returning a context mutator, header lookup, field merging with overwrite control, and early returns for missing data \n Requires updates to enricher source code, test suite, built-in documentation, overview cards, public skill reference, and README enrichers table \n All six touchpoints are mandatory; the

Trace upstream data lineage to identify sources, DAGs, and dependencies feeding a table or column. \n \n Supports tracing three target types: tables, columns, and DAGs; uses Airflow DAG source code and task inspection to find producing pipelines \n Handles SQL sources (FROM clauses), external systems (S3, Postgres, Salesforce, HTTP APIs), and file-based sources; recursively traces upstream chains \n Includes column-level tracing through direct mappings, transformations, and aggregations in DAG c

Important: This skill assists with journal entry workflows but does not provide financial advice. All entries should be reviewed by qualified financial professionals before posting.

Register, schedule, and execute background work on iOS using the BackgroundTasks framework, background URLSession, and background push notifications.

Swap a face from a source image into a target video using GPU-accelerated AI processing. The source image provides the face to swap in, and the target video receives the new face.

Data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib. \n \n Covers DataFrame and Series creation, indexing, filtering, and type conversions for structured data handling \n Includes data cleaning techniques: missing value handling, deduplication, string operations, and date/time parsing \n Provides GroupBy aggregation, pivot tables, multi-level indexing, and window functions for exploratory analysis \n Integrates Matplotlib and Seaborn for statistical plotting, trend

Expert guidance for building retrieval-augmented generation systems with optimized embeddings, chunking, and retrieval pipelines. \n \n Covers semantic chunking, hierarchical retrieval, and hybrid search combining keyword and vector similarity matching \n Addresses critical RAG pitfalls including fixed-size chunking, embedding refresh strategies, and retrieval evaluation separate from generation quality \n Emphasizes chunking by meaning rather than token limits, multi-level indexing for precisio

Create, extract, and apply portable visual design systems. A visual-style.md file defines colors, typography, layout, motion, and mood in one file that any AI tool can consume.

Automate pull request creation with formatted commits and branch management. \n \n Ensures you're not on the main branch, creating a feature branch if needed \n Runs Oxfmt code formatting across all affected packages before committing \n Formats commits and PR titles using package name from package.json (e.g., \" @remotion/shapes : Add heart shape\") \n Uses the gh CLI to open the pull request automatically after pushing changes \n

Design patterns for building autonomous coding agents with tool integration, permissions, and human oversight. \n \n Core agent loop architecture with think-decide-act-observe cycle; supports multi-model selection for different task types \n Tool design patterns including file operations, code understanding, terminal execution, and browser automation with schema-based definitions \n Permission system with four levels (auto, ask-once, ask-each, never) and risk assessment for approval workflows \n