MCKRUZ

Build identity-preserving character generation workflows and pipelines in ComfyUI. Selects the optimal identity method (InfiniteYou, FLUX Kontext, PuLID, InstantID, IP-Adapter) based on use case requirements. Handles face preservation, likeness transfer, cross-domain conversion (3D to photo), multi-reference consistency, iterative character editing, and character variation generation. Triggers on requests to generate consistent characters, preserve identity across images, create face-swapping workflows, or convert 3D renders to photorealistic portraits. Does NOT cover general image generation without identity preservation, model training/LoRA fine-tuning, animation, technical explanations, or workflow debugging.

Guided conversational interview to understand a user's creative vision before generating model-appropriate image prompts. Asks clarifying questions about subject, mood, style, and technical preferences (4-7 exchanges), then synthesizes positive prompt, negative prompt, recommended settings table, and pipeline recommendation. Formats prompts for the target model (SDXL tag-style, FLUX natural language, SD1.5 weighted tokens). Triggers on "I want to create...", "help me make an image of...", "I have an idea for...", "help me craft a prompt", "write me a prompt for...", or any request for help describing a creative vision. Does NOT cover workflow building, prompt debugging/fixing, technical explanations, model training, code generation, or identity-preserving character generation.

Generate videos using ComfyUI with Wan 2.2, FramePack, or AnimateDiff. Handles image-to-video, text-to-video, talking heads, and motion-controlled animation. Use when creating any video content from character images or text descriptions.

Plan and orchestrate end-to-end video production pipelines in ComfyUI with validation gates and error recovery. Handles img2vid, txt2vid, vid2vid, and multi-shot video production. Produces pipeline plans with correct step ordering (generate, validate, animate, validate, concat), model selection, retry strategies (seed randomization, parameter adjustment, model fallback), and VRAM-aware resource management. Use when asked to make a video, animate images, create a multi-shot video, set up a video pipeline, or orchestrate video production in ComfyUI. Does NOT cover still image generation, prompt writing, workflow building for non-video tasks, video editing in external tools, model training, installation, or hardware recommendations.

Generate, build, create, or design ComfyUI workflow JSON from natural language descriptions. Produces valid node graphs with correct class_types, connections, output indices, and model-appropriate settings. Handles txt2img, img2img, inpainting, ControlNet, LoRA stacking, upscaling, and face detailing pipelines. Does NOT cover ComfyUI installation, custom node development, Python scripting, model training, hardware advice, or architectural explanations.

Connect to a running ComfyUI instance, queue workflows, monitor execution, and retrieve results. Supports both online (REST API) and offline (JSON export) modes. Use when executing ComfyUI workflows or checking server status.

Discover and cache all installed ComfyUI models, custom nodes, and system capabilities. Works online (API queries) and offline (directory scanning). Use before generating workflows to verify available resources.

Prepare datasets and configure LoRA training for character consistency. Covers FLUX (AI-Toolkit, SimpleTuner, FluxGym) and SDXL (Kohya_ss) training with step-by-step guidance. Use when training custom character LoRAs.

General-purpose conversational assistant that reads project files and renders images, forms, and tables inline in its answers.

Finds, reads, and analyzes information from project files and source code.

Conversational agent embedded in the observability dashboard. Reads the current view and acts on it — changes the time range, navigates between pages, and refreshes data — and can list the available metrics.

Scaffold a Terraform or Bicep module, validate and plan it, and security-scan it before proposing the change. Plan and scan only — this skill never deploys.

Root-cause cluster problems with K8sGPT analysis and live health, without mutating the cluster. Returns LLM-shaped findings; remediation is out of scope for this skill.

Retrieve past GitOps learnings, incidents, and resolutions from the knowledge base and correlate them with live K8sGPT analysis. Read-only — retrieval and reasoning, no cluster or repo changes.

Detect drift between desired Git state and the live cluster, read cluster health, and propose a Git-side remediation as a ChangeProposal. Never mutates the cluster or repo directly.

Read, list, write, and verify files in a sandbox-injected working copy. Write goes through ChangeProposal — never direct mutation.

SDLC orchestration plugin for Claude Code. Drives projects through a 9-phase software development lifecycle with company-configurable profiles, compliance gates, and quality enforcement. Triggers: "start sdlc", "sdlc setup", "initialize project lifecycle", "run phase gate", "advance phase", "sdlc status", "compliance check", "project setup wizard", "lifecycle management".

Add a new endpoint / route / use case / service the way this Node + TypeScript codebase already does it. Use when building an API surface, a request handler, a validator, a port/adapter, or a container registration in a Node/TypeScript repo. Finds the one canonical example and mirrors its layering, validation, error model, and test shape — so agents extend the codebase instead of inventing a second way to do something it already does. The Node realization of the core api-pattern skill.

Add a new module / CLI command / service function / loader the way this Python codebase already does it. Use when building a public surface, a command entry point, a data loader, a validator, or a new package module in a Python / uv repo. Finds the one canonical example and mirrors its layering, typing, error model, and test shape — so agents extend the codebase instead of inventing a second way to do something it already does. The Python realization of the core api-pattern skill.

Build a golden set for an agentic (LLM-powered) spec — the acceptance criteria for probabilistic behavior. Use when a spec's deliverable is an agent/prompt/tool and needs evals before build, or when adding cases from a production failure. Produces a versioned golden-set.yaml under eval-datasets/specs/<feature>/, referencing the spec file.

Add a new endpoint / MediatR command or query / handler / service the way this .NET codebase already does it. Use when building an API surface, a CQRS handler, a controller, a validator, or a DI registration in a .NET / Clean Architecture repo. Finds the one canonical example and mirrors its layering, validation, error model, and test shape — so agents extend the codebase instead of inventing a second way to do something it already does. The .NET realization of the core api-pattern skill.

Add a new endpoint / handler / service the way this codebase already does it. Use when building an API surface, a CQRS handler, a controller, or a service registration. Finds the one canonical example and follows it exactly — so agents reuse the established pattern instead of inventing a new one each run.

The team's bug-fix procedure: reproduce with a FAILING TEST first, then fix, then prove the test passes. Use whenever a bug is reported. Do not start by patching — start by reproducing. For deep root-cause hunts, invoke the debugger agent from inside this procedure.

Open a spec's PR correctly: the right branch, a conventional-commit title, a body that maps the diff to the spec's acceptance checks with a test plan, and the spec file included in the diff. Use when a change is ready to push for review.

Interacts with GitHub using the EXTERNAL GitHub MCP Server (@modelcontextprotocol/server-github). Can search repos, list issues, get file contents, and more. USES EXTERNAL MCP SERVER.

Explains code in plain English. Paste any code snippet and get a clear, detailed explanation of what it does, how it works, and why it's written that way. No tools required - pure LLM reasoning.

Analyzes codebases using custom MCP tools. Provides directory structure, lines of code metrics, and finds TODOs/FIXMEs. USES CUSTOM MCP TOOLS.

Use this skill when the user asks to "refresh research", "update the catalog", "re-run the framework research", "check for new methodologies", "update mck-scaffold", or when /mck-scaffold:init Step 0 detects a stale `research/last-run.json` and the user opts in. Orchestrates 4 parallel research subagents (GitHub / Reddit / YouTube / web-blogs), diffs new findings against the current catalog in `research/streams/*.md`, surfaces the diff for user-confirmed application to `interview/routing.md` and methodology preset templates, then updates `research/last-run.json` + `research/changelog.md`.

Use this skill when the user asks to "set up a new project", "start a new project", "scaffold a project", "pick a methodology", "choose an SDD framework", "configure Claude Code for this project", "what methodology should I use", or discusses agentic development methodology selection. Helps the user route to the right Spec-Driven Development approach based on Piskala's three-level rigor taxonomy and Wasowski's four decision contexts. Does NOT write files — conversational guidance only. For actual scaffolding, route to /mck-scaffold:init.

Initialize a new Claude Code project with a tailored Spec-Driven Development methodology. Runs an interview to determine the right approach (L1 Spec-First / L2 Spec-Anchored / L3-watch), composes templates, writes the project's CLAUDE.md / AGENTS.md / .claude/settings.json / decision docs, and triggers upstream framework installs (Superpowers, Spec-Kit, MUSUBI, OpenSpec). Invoked via /mck-scaffold:init.

The definitive Claude Code setup, configuration, and mastery skill. Use when setting up Claude Code for a new project, optimizing an existing setup, configuring CLAUDE.md files, MCP servers, hooks, permissions, agent teams, skills, plugins, skill development, eval frameworks, or CI/CD integration. Triggers on: "set up Claude Code", "configure CLAUDE.md", "optimize my Claude Code setup", "MCP server", "agent teams", "Claude Code hooks", "Claude Code permissions", "Claude Code CI/CD", "Claude Code best practices", "improve my Claude Code workflow", "skill development", "eval framework", "build a skill", or any question about Claude Code configuration and architecture. Does NOT trigger on general coding tasks, code review, debugging, or tool configuration unrelated to Claude Code.