Access Adobe Creative Cloud APIs - Photoshop, Lightroom, PDF Services, and Firefly AI. Automate creative workflows.

Manage enterprise e-signatures with Adobe Sign's document signing solution.

Structured guide for designing and executing customer adoption programs.

Use when documenting significant technical or architectural decisions that need context, rationale, and consequences recorded. Invoke when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when team needs to understand why a decision was made. Use when user mentions ADR, architecture decision, technical decision record, or decision documentation.

Helps create, analyze, and maintain Architecture Decision Records

description: Automatically load and apply relevant ADRs when creating or modifying resources

Validate code changes against Architecture Decision Records (ADRs)

Add ADR references to code for traceability. TRIGGERS - ADR traceability, code reference, document decision in code.

Generate Architecture Decision Records following the project template and numbering convention. Use when documenting architecture decisions, technical choices, or when the user asks to create an ADR.

Create Architecture Decision Records (ADRs) that document significant technical decisions for the Fantasy Football Analytics project following the established ADR format and workflow.

Specialized skill for generating and managing Architecture Decision Records (ADRs). Supports Nygard, MADR, and custom templates with auto-numbering, linking, and status management.

ASCII architecture diagrams for ADRs via graph-easy. TRIGGERS - ADR diagram, architecture diagram, ASCII diagram.

Document architecture decisions with ADR (Architecture Decision Records). Use when making significant technical decisions, choosing between alternatives, or when onboarding needs context on past decisions.

Create and manage Architecture Decision Records (ADRs). Use when documenting technology choices, design decisions, or architectural changes that need to be tracked over time. This is the CANONICAL ADR skill - all ADR-related work should use this skill.

Structured frameworks for documenting architectural decisions with human-in-the-loop AI assistance.

Multi-agent debate orchestration for Architecture Decision Records. Automatically triggers on ADR create/edit/delete. Coordinates architect, critic, independent-thinker, security, analyst, and high-level-advisor agents in structured debate rounds until consensus.

Generate phased implementation roadmaps from Architecture Decision Records

Create Architecture Decision Records through interactive conversation. Use when making technology choices, architecture patterns, or third-party service selections.

Documenting significant architectural decisions with context, consequences, and rationale for future reference.

Document architecture decisions with clear context, alternatives, and consequences.

Manage Architecture Decision Records (ADRs) throughout their lifecycle: read existing decisions, write new ones, and ensure development aligns with documented decisions.

Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:

Advanced debugging skill for MyJKKN project. Specialized workflows for debugging Next.js 15, Supabase, React Query, TypeScript, and service layer issues. Includes automated analysis tools, common error patterns, and step-by-step troubleshooting guides for reducing debugging time. Use when investigating bugs, errors, performance issues, or unexpected behavior. (project)

Advanced example showing all available metadata fields and complex folder structure

advanced-features-2025

Advanced GetX features including Workers, GetxService, SmartManagement, GetConnect, GetSocket, bindings composition, and testing patterns

Custom Resource Definitions (CRDs) extend Kubernetes API with custom

Probability, moments/tails, Bayes, and statistical learning foundations for systematic trading.

Factor modeling and portfolio construction (Markowitz, Black-Litterman, constraints, turnover).

Tail risk, EVT, regularization, validation guardrails, and common pitfalls.

Apply principled memoization techniques to reduce re-rendering without introducing correctness bugs.

Use when planning, scaffolding, validating, or packaging Claude skills inside Advanced Memory MCP.

Perform comprehensive OSCAL validation using community-inspired patterns including JSON schema validation, business rule validation, cross-reference checking, and best practices from IBM Trestle, oscal-pydantic, and Lula. Use for thorough document quality assurance.

Specialized reverse engineering analysis workflows for binary analysis, pattern recognition, and vulnerability assessment

Master high-performance rendering for large datasets with Datashader. Use this skill when working with datasets exceeding 100M+ points, optimizing visualization performance, or implementing efficient rendering strategies with rasterization and colormapping techniques.

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Engineer SSR caching strategies that preserve correctness under concurrent updates and streaming.

Implement AI-powered statusline with session tracking, plan detection, workspace emojis, and intelligent caching for Claude Code

Production-grade text search algorithms for finding and matching text in large documents with millisecond performance. Includes Boyer-Moore search, n-gram similarity, fuzzy matching, and intelligent indexing. Use when building search features for large documents, finding quotes with imperfect matches, implementing fuzzy search, or need character-level precision.

Download and transcribe videos from YouTube, Bilibili, TikTok and 1000+ platforms. Use when user requests video download, transcription (转录/字幕提取), or converting video to text/markdown. Supports quality selection, audio extraction, playlist downloads, cookie-based authentication, and AI-powered transcription via SiliconFlow API (免费转录).

advanced

Room-based exploration with narrative evidence collection

Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness

adversarial-review

Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.

Interactive conversational guidance - user implements with step-by-step advice. Use when you want hands-on implementation with expert guidance while maintaining control.

Detects when user requests warrant critical analysis via /advise command

Recruit, structure, and manage advisory boards for strategic guidance

Use when designing and scaling reference, story, advisory, or community