legout

Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact. Use when the user wants to start a marimo notebook or work in an active marimo session.

Use br (beads_rust) as the local issue tracker for AI coding agents. Trigger when the user mentions issues, tickets, backlog, triage, dependencies, blocking, ready work, or any task involving tracking work items in a git repo. Also trigger when the user wants to plan work, pick next tasks, manage a dependency graph of issues, or coordinate across multiple agents. Use this skill for commands like br create, br ready, br close, br dep, br sync, and any br CLI interaction. Even if the user just says "what should I work on next" or "create a ticket for this", use this skill to ensure correct br usage patterns.

Design and implement practical AI agents using a production-oriented framework. Use this skill whenever the user wants to build an AI agent, decide whether a workflow should become an agent, choose between single-agent and multi-agent designs, define tools/instructions/guardrails, add human-in-the-loop controls, or scaffold an agentic workflow for real systems. Trigger for requests about agents, copilots, tool-using assistants, orchestration, handoffs, manager-worker patterns, agent guardrails, or productionizing multi-step LLM workflows even when the user does not explicitly ask for an “agent architecture.”

Generate or discover a CLI + AgentSkill for any REST API. Use when: user says 'create a CLI for X', 'wrap this API', 'search if a CLI exists', 'install an existing CLI', 'make a skill for X', 'publish my CLI', 'publish to npm', 'push to github'. Handles registry search, install, discovery, scaffolding, resource implementation, building, linking, skill generation, npm publishing, and GitHub publishing.

**[REQUIRED]** Use for ALL Streamlit tasks: creating, editing, debugging, beautifying, styling, theming, optimizing, or deploying Streamlit applications. Also required for building custom components (inline or packaged), using st.components.v2, or any HTML/JS/CSS component work. Triggers: streamlit, st., dashboard, app.py, beautify, style, CSS, color, background, theme, button, widget styling, custom component, st.components, packaged component, pyproject.toml, asset_dir, CCv2, HTML/JS component.

Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.

Access cloud storage (S3, GCS, Azure) in Python using fsspec, pyarrow.fs, or obstore. Includes DataFrame integrations (Polars, DuckDB, Pandas, PyArrow), performance optimization, patterns for incremental loading, partitioned writes, and cross-cloud copy.

Exploratory data analysis and visualization: profiling datasets, choosing appropriate charts, applying statistical tests, and creating effective visualizations for insight communication. Use when understanding data structure, exploring distributions and relationships, selecting visualization libraries, or producing analysis-ready charts.

Data quality validation and observability for data pipelines. Combines Great Expectations and Pandera for data validation with OpenTelemetry and Prometheus for monitoring and alerting.

File formats and lakehouse table formats for data lakes: Parquet, Arrow, Lance, Zarr, Avro, ORC, Delta Lake, Apache Iceberg, and Apache Hudi. Covers compression, partitioning, ACID transactions, schema evolution, and format selection.

AI/ML production workflows: embedding generation, vector storage, RAG patterns, LLM monitoring, and batch inference.

Feature engineering for machine learning: encoding categorical variables, scaling numeric features, datetime transformations, text features, and leakage-safe preprocessing pipelines. Use when preparing data for modeling or improving model performance through better representations.

Model evaluation and validation: cross-validation strategies, metrics selection, hyperparameter tuning, experiment tracking, and model comparison. Use when assessing model performance, diagnosing issues, selecting models, or optimizing hyperparameters.

Create and manage data pipelines using the FlowerPower framework with Hamilton DAGs and uv. Lightweight orchestration for batch ETL, data transformation, and ML pipelines. Integrates with Delta Lake, DuckDB, Polars, and cloud storage.

Sequentially drain ready br issues or tk tickets through the existing ship chains with retry limits and progress state.

Update a work item with a concise execution note and close it only when validation and review evidence prove the acceptance criteria. Use for prompts that finalize work-item status after manual execution chains.

Review work items, ExecPlan delivery, or arbitrary code changes with evidence-backed findings. Reviews are read-only by default and create tracker follow-ups only when explicitly requested with --create-followups.

Implement or fix a work item using minimal diffs while preserving scope, local conventions, and testability. Use for prompts that change code or apply validation/review fixes.

Validate work-item changes using concrete evidence, exact commands, and explicit gap reporting. Use for validation prompts and review passes that audit proof.

Create a self-contained ExecPlan from a brainstorm artifact and/or a user brief. Follows the project's .execflow/PLANS.md spec. Applies the Ousterhout design lens. Use when creating execution plans, turning ideas into implementable steps, or wants a PRD/RFC/brainstorm turned into a step-by-step plan.

Convert an ExecPlan's milestones into tracked work items, automatically selecting tk tickets or br issues from repo context unless the user forces a tracker with --tk or --br. Use when the user wants one entrypoint that works across both tracker backends.

Interactive design brainstorming for a topic or feature idea. Explores project context before implementation, asks one focused question at a time, compares 2-3 approaches, obtains user approval, writes a reviewed brainstorm/design artifact under .execflow/plans/, and gates transition to ExecPlan creation.

Execute one pre-approved GitHub issue unattended using a just-in-time Superpowers plan, strict escalation boundaries, verification, and PR handoff.

Run Matt Pocock's two-axis code review through Pi Superagents for an unattended pi-superworkers issue worker.

Review an unattended worker's implementation plan against its GitHub issue, parent requirements, repository rules, ADRs, and current code before implementation begins.

Create and manage data pipelines using the FlowerPower framework with Hamilton DAGs and uv. Use when users request creating flowerpower projects, pipelines, Hamilton dataflows, or ask about flowerpower configuration, execution, or CLI commands.