Machine Learning

Systematic debugging techniques including error classification, root cause analysis (5 Whys), reproduction strategies, and error documentation. Use when debugging errors, investigating failures, analyzing stack traces, fixing bugs, or documenting errors in error-log.md. (project)

Decomposes large features into concrete development phases with DAG dependencies. Each phase produces fully functional code. Outputs YAML manifest. Triggers on keywords: decompose feature, phase planning, roadmap breakdown, feature phases, PM decomposition, split feature

Enforce consistent theming across all prototype mockup screens. Auto-loads theme.yaml, validates CSS variable usage, prevents hardcoded values, and maintains visual consistency session-to-session. Use during /prototype create when generating new screens.

Manage YAML frontmatter properties with consistent formatting, property names, and value types. Use when creating or updating frontmatter in markdown files.

Creates or updates .daiv.yml configuration file with sandbox settings (base_image and format_code commands) based on repository content. Use when users request DAIV configuration setup or sandbox configuration.

Generate Docker Compose and Dockerfile configurations for local development through interactive Q&A. Supports PHP/Laravel, WordPress, Drupal, Joomla, Node.js, and Python stacks with Nginx, Supervisor/PM2, databases, Redis, and email testing. Always asks clarifying questions before generating configurations.

UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient.

Comprehensive CI/CD pipeline methodology with quality gates, release automation, smoke testing, observability, and performance tracking. Use when setting up CI/CD from scratch, build time over 5 minutes, no automated quality gates, manual release process, lack of pipeline observability, or broken releases reaching production. Provides 5 quality gate categories (coverage threshold 75-80%, lint blocking, CHANGELOG validation, build verification, test pass rate), release automation with conventional commits and automatic CHANGELOG generation, 25 smoke tests across execution/consistency/structure categories, CI observability with metrics tracking and regression detection, performance optimization including native-only testing for Go cross-compilation. Validated in meta-cc with 91.7% pattern validation rate (11/12 patterns), 2.5-3.5x estimated speedup, GitHub Actions native with 70-80% transferability to GitLab CI and Jenkins.

HTML 슬라이드를 PowerPoint(PPTX) 파일로 변환. PPTX 생성, 편집, 썸네일 생성이 필요할 때 사용.

专业的 Dify 工作流 DSL/YML 文件生成器，根据用户业务需求自动生成完整的 Dify 工作流配置文件，支持各种节点类型和复杂工作流逻辑

프레젠테이션 슬라이드를 미려한 HTML로 디자인. 슬라이드 HTML 생성, 시각적 디자인, 레이아웃 구성이 필요할 때 사용.

Systematic bug fixing workflow with regression tests and PR creation. Use when the user asks to "fix a bug", "debug an issue", "resolve a problem", or provides error messages/failing tests to fix. Handles reproduction, root cause analysis, test creation, fix implementation, and PR submission.

Analyze WebAssembly test (WAST) files to debug compilation issues and create regression tests. Use when the user asks to debug or analyze WAST test failures, investigate compilation bugs in wasmoon, or when encountering test failures in spec/*.wast files. Triggers include "analyze wast", "debug wast", "wast bug", or references to specific .wast test files.

Guide for debugging and fixing bugs in the OCaml garbage collector, particularly memory management issues in the runtime's sweeping and allocation code. This skill applies when working on OCaml runtime C code, investigating segfaults in GC operations, or fixing pointer arithmetic bugs in memory managers with size-classed pools and run-length encoding.

This skill provides guidance for training FastText text classification models with constraints on accuracy and model size. It should be used when training fastText supervised models, optimizing model size while maintaining accuracy thresholds, or when hyperparameter tuning for text classification tasks.

Guide for debugging and fixing bugs in the OCaml garbage collector runtime, particularly issues in memory management code like sweeping, pool management, and run-length compression. This skill should be used when encountering segfaults during OCaml bootstrap, debugging crashes in runtime/shared_heap.c or similar GC code, or investigating pointer arithmetic bugs in memory allocator implementations.

Authoritative templates and scaffolding for creating slash commands. This skill should be usedwhen creating new commands, reviewing existing commands for template compliance, or understandingcommand structure. Contains YAML templates with section instructions and scaffolding scriptsfor generating command skeleton files.

Manage backlog items across multiple backends (files, GitHub Issues, Linear, Beads). Configure task_management in .agents.yml.

Guidance for optimizing MuJoCo MJCF model files for simulation performance while maintaining numerical accuracy. This skill should be used when tuning physics simulation parameters, optimizing MuJoCo XML configurations, or balancing speed vs accuracy tradeoffs in robotics simulations.

Worker that runs existing tests to catch regressions. Auto-detects framework, reports pass/fail. No status changes or task creation.