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Found 3,219 Skills
Prepare a day-one patch for a game launch. Scopes, prioritises, implements, and QA-gates a focused patch addressing known issues discovered after gold master but before or immediately after public launch. Treats the patch as a mini-sprint with its own QA gate and rollback plan.
Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
Complete launch readiness validation covering every department: code, content, store, marketing, community, infrastructure, legal, and go/no-go sign-offs.
Converts any Claude Code skills repository into an official plugin marketplace. Analyzes existing skills, generates .claude-plugin/marketplace.json conforming to the Anthropic spec, validates with `claude plugin validate`, tests real installation, and creates a PR to the upstream repo. Encodes hard-won anti-patterns from real marketplace development (schema traps, version semantics, description pitfalls). Use when the user mentions: marketplace, plugin support, one-click install, marketplace.json, plugin distribution, auto-update, or wants a skills repo installable via `claude plugin install`. Also trigger when the user has a skills repo and asks about packaging, distribution, or making it installable.
Styled text display and rich text editing in SwiftUI using Text, AttributedString, and TextEditor with formatting controls. Use when implementing rich text editing or styled text display.
Automates codebase environment configuration, troubleshooting, and repair. When non-technical users (editors, business personnel, operations staff) get a repository and say things like "it won't run", "how to start", "how to configure the environment", "help me set up the codebase", "initialize the project", "commit code", "what to do about conflicts", it automatically reads ONBOARDING.md, diagnoses environment gaps, fixes dependencies, verifies runnability, and safely completes git operations. It is also used by technical users to quickly standardize the setup process for new repositories (SessionStart hook, PII Guard, history sanitization, project-isolated API keys). This skill is triggered whenever users mention terms like "environment", "configuration", "won't run", "setup", "start", "clone", "how to run", "dependencies", "is it installed", "commit code", "merge conflict", "push failed".
Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
Create SEO-optimized marketing content with consistent brand voice. Includes brand voice analyzer, SEO optimizer, content frameworks, and social media templates. Use when writing blog posts, creating social media content, analyzing brand voice, optimizing SEO, planning content calendars, or when user mentions content creation, brand voice, SEO optimization, social media marketing, or content strategy.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.