Use when creating SVG shapes, paths, or generators for charts. Invoke for line/area/arc generators, curves, symbols, pie/donut charts, stacks, ribbons, or path manipulation operations.

Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).

Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).

Expert on building decentralized applications with Shelby Protocol storage on Aptos. Helps with dApp architecture, wallet integration (Petra), browser SDK usage, React/Vue integration, file uploads, content delivery, and building Shelby-powered applications. Triggers on keywords Shelby dApp, build on Shelby, Shelby application, Petra wallet, browser storage, web3 app, decentralized app Shelby, React Shelby, Vue Shelby.

To apply automated Dart fixes, run `dart fix --apply` on the given roots to resolve suggested changes.

Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment

Dashboard symbol_signals uses parallel lists (symbols[], signal_values[], gate_statuses[]) not dict keyed by symbol. Trigger when: (1) 'list' object has no attribute 'get', (2) .items() on symbol_signals fails.

Trading dashboard P&L visualization with profit tracker integration, win-rate overlays, R-multiples, and configurable settings

Auto-discover dashboard symbols from loaded RL models. Trigger when: (1) dashboard shows old/wrong symbols, (2) symbols mismatch between live trader and dashboard, (3) adding new models to system, (4) dashboard shows NO_MODEL for all symbols.

Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.

Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.

Clean and standardize vehicle insurance data following established business rules.

SWR-based data fetching and caching patterns used throughout the monorepo. Use this skill when implementing API interactions, creating custom data hooks, handling loading/error states, or working with mock data. Covers SWR configuration, custom hook patterns (useUserInfo, useTimesSquarePage), error handling, and mock data setup.

Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.

Service-scoped data orchestration for TMNL. Invoke when implementing search, data streams, kernel systems, or Effect-based DAQ. Covers hybrid dispatch (fibers + workers), Atom-as-State pattern, and progressive streaming.

Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).

Set up database replication for high availability and disaster recovery. Use when configuring master-slave replication, multi-master setups, or replication monitoring.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.

Query logs, metrics, monitors, and dashboards from Datadog. Search logs, check alert status, and investigate incidents.

Use when editing Planning Hubs, timelines, calendars, or any file with day-name + date combinations (Wed Nov 12), relative dates (tomorrow), or countdowns (18 days until) - validates day-of-week accuracy, relative date calculations, and countdown math with two-source ground truth verification before allowing edits

Transform AI agents into experts on dbt project architecture and medallion layer patterns, providing

Transform AI agents into experts on writing production-quality dbt models, providing guidance on CTE

Use when making architectural or business logic decisions during conversations - adds entry to DECISIONS.md

決策樹助手工具。快速評估任務複雜度，提供派發建議。用於: (1) 任務複雜度快速評估, (2) 派發代理人建議, (3) 拆分策略建議, (4) 並行可行性評估

Expert guidance for DeepAgents framework - simplified agent creation with tool integration for LangChain/LangGraph workflows.

Use this skill in the scenario of deep learning project development.

'Create a DESIGN_GUIDELINES.md that defines how to design UI/UX for your customer. Requires CUSTOMER.md to exist first. Covers aesthetic direction, design tokens, typography, color, motion, components, and layout patterns. Bakes in frontend-design skill principles to avoid generic AI aesthetics.'

Use when designing cloud infrastructure, CI/CD pipelines, or deployment strategies

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dependency-vetting

Map and visualize dependencies between features and work items.

Deploy Frappe HRMS code changes to AWS production. Use when you need to deploy Python/API changes to the Frappe Docker container.

Comprehensive guide for deploying Orient to production. Use this skill when deploying changes, updating production, fixing deployment failures, or rolling back. Covers pre-flight checks, environment variables, Docker compose configuration, CI/CD pipeline, smart change detection, and health verification.

Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.

Automates GitHub repository creation and Vercel deployment for Next.js websites. Use when deploying new websites, pushing to production, setting up CI/CD pipelines, or when the user mentions deployment, GitHub, Vercel, or going live.

Expert guide for deploying Next.js apps to Vercel, managing environments, CI/CD pipelines, and production best practices. Use when deploying, setting up automation, or managing production.

Document deployment processes, infrastructure setup, CI/CD pipelines, and configuration management. Use when creating deployment guides, infrastructure docs, or CI/CD documentation.

This skill should be used when the user asks about deploying Rails applications, Kamal deployment, Docker containers, production configuration, environment variables, secrets management, CI/CD pipelines, server provisioning, zero-downtime deploys, Kamal Proxy, Thruster, or infrastructure setup. Also use when discussing production optimization, deployment strategies, or hosting options. Examples:

CI/CD pipelines, zero-downtime deployments, infrastructure as code, and production deployment strategies

Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.

You are operating with design decision logging capabilities. This skill ensures that every significant design choice has documented reasoning — transforming unconscious defaults into conscious decisio

Stand up a Design Engineering practice (hybrid design+engineering) by producing a Design Engineering Execution Pack: charter, prototype→production workflow, design-to-code contract, component delivery plan, and quality bar. Use for design engineering, UI engineering, design systems engineering, and prototype-to-production processes.

Establish or formalize your design system foundation. Create design tokens (color, typography, spacing, shadows, borders), define component architecture, document design principles, and build the structure that enables consistency and scalability. Works with Tailwind CSS and framework-agnostic approaches.

MLP Finanzberatung SE Corporate Design System combined with modern UI principles. Use when creating any UI, presentations, documents, or materials for MLP financial advisors. Ensures brand-compliant colors, typography, accessibility standards, consistent spacing (8px grid), and subbrand guidelines for medical professionals (MLP Med) and education (SOFE).

Master expert in product design for complex systems and enterprise SaaS with 10+ years of experience in data-driven systems, operational dashboards, Incident Management, NOC, DevOps, Observability, an

Complete Design System Reverse Engineering (DSRE) + Functional Cloning CLI - Extract visual design (tokens, assets) AND functional behavior (workflows, state machines, business rules) from any web application

Managing design tokens and system context for LLM-driven UI development. Covers loading, persisting, and optimizing design decisions within context windows.