Run a comprehensive, read-only health check on the user's computer and return a scored chat summary with prioritized, plain-English recommendations and safe cleanup suggestions. Use this whenever the user wants to check their computer's health, speed it up, free up / reclaim disk space, find what's eating CPU / memory / storage, check battery health and wear, audit login & startup items, see pending updates, or asks for a "tune-up", "checkup", "system report", "health check", or "is my Mac/PC/laptop healthy". Trigger even on casual phrasing like "my laptop feels slow", "why is my fan so loud", "my computer is laggy", "running out of space", or "clean up my machine". Works on macOS, Windows, and Linux. This is a Desktop Commander skill: it relies on a real local shell (start_process / interact_with_process) and is strictly read-only by default — it never needs sudo and only performs cleanups the user explicitly approves.

Audits Convex performance for reads, subscriptions, write contention, and function limits. Use for slow features, insights findings, OCC conflicts, or read amplification.

Create precise SVG technical diagrams, export PNG or offline HTML, and animate supported semantic SVGs to GIF. Use for architecture, UML, agent, cloud or workflow diagrams; not photos, raster art or statistical charts.

Create or update high-quality Linear tickets while preserving the originating report, diagnostic links, attachments, and reporter/requester attribution. Read this whenever someone asks to create, recreate, or update a Linear issue.

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

Design a Rerun blueprint from the data, then iterate on it from headless screenshots. Read this when laying out a recording or dataset in the viewer, designing a default blueprint, or deciding which views show which entities. Covers archetype-to-view mapping, layout reasoning, the rrb construction API, the contents grammar, and the screenshot loop.

Use this skill for failed MoviePilot video or music transfer history IDs. Inspect the exact records, preserve durable retry evidence, group only records with a trustworthy shared identity, and re-identify/reorganize only legacy records that were actually deleted.

Use this skill when a user provides a torrent name or file name and wants to fix recognition issues, or asks to add/manage custom identifiers (自定义识别词). This skill generates identifier rules based on the WordsMatcher preprocessing logic, checks for duplicates against existing rules, and saves them through the structured MoviePilot API gateway. Because custom identifiers are global, generated rules must default to conservative, sample-specific regex patterns instead of broad matches unless the user explicitly wants global cleanup. Applicable scenarios include: 1) A torrent or file name is incorrectly recognized (wrong title, season, episode, etc.); 2) The user wants to block unwanted keywords from torrent names; 3) The user needs episode offset rules for series with non-standard numbering; 4) The user wants to force recognition of a specific media by source-native ID; 5) The user wants TV recognition to use a specific TMDB episode group.

Use this skill when the user asks the agent to open, browse, inspect, extract content from, click through, fill forms on, screenshot, or verify a web page with a browser. Also use it for MoviePilot scenarios that need browser interaction, such as checking a site page, confirming a JavaScript-rendered result, testing login state, capturing visible errors, or updating and validating tracker site cookies.

Decide and execute plain-file decomposition for OpenTelemetry Collector YAML. Use for "should I split this collector config?", "break up this otelcol YAML", "organize it by signal or team", or "create environment overlays". Leaves simple configs intact and proves warranted splits equivalent after merge. Not for Helm, Kustomize, or Terraform packaging; use otel-collector for component facts and ollygarden-otel-collector-config-validation for behavioral proof.

Create a suitable, shareable Tranmere Rovers-inspired player avatar with Tranmere-Web’s Avatar Studio. Use when asked to turn a player description, an attached photo, or public web photo references into a stateless avatar URL and inline preview; use for real players, historical players, fan-made players, and fictional Rovers characters.

Operate Claude Code CLI (v2.1+) via the terminal only when the user explicitly requests Claude Code or names this skill. Covers print mode (-p), interactive tmux sessions, and background (--bg) orchestration.

Operate Claude Code CLI (v2.1+) via the terminal only when the user explicitly requests Claude Code or names this skill. Covers print mode (-p), interactive tmux sessions, and background (--bg) orchestration.

Design, build, verify, and publish production-grade TypeScript SDKs as npm packages inside a pnpm monorepo. Covers workspace layout, public API and module boundaries, plugin extension points, branded types and library-tuned tsconfig, tsdown bundling (vs tsup/tsc-only/unbuild), package.json exports with dual ESM+CJS and isomorphic conditions (browser/workers/RN/deno), Turborepo pipelines, publint and @arethetypeswrong/cli verification, changesets pre-release mode, npm dist-tags (latest/next/beta/rc/canary), and the alpha→beta→rc→stable release lifecycle. Triggers on: build a TS SDK, extract core library, package.json exports, dual ESM CJS, tsdown config, tsup vs tsdown, publint, attw, changesets prerelease, npm dist-tag, beta to rc, canary release, pnpm workspace SDK, isomorphic SDK, tsconfig library, npm provenance, shipping a TypeScript library.

Bootstrap project-specific Trellis coding specs with a platform-neutral single-agent workflow. Use when creating or refreshing .trellis/spec guidelines, analyzing a codebase with GitNexus, ABCoder, or source inspection, decomposing package/layer spec work, and writing real codebase-backed spec docs without placeholder text.

Bootstrap project-specific Trellis coding specs with a platform-neutral single-agent workflow. Use when creating or refreshing .trellis/spec guidelines, analyzing a codebase with GitNexus, ABCoder, or source inspection, decomposing package/layer spec work, and writing real codebase-backed spec docs without placeholder text.

Discovers and injects project-specific coding guidelines from .trellis/spec/ before implementation begins. Reads spec indexes, pre-development checklists, and shared thinking guides for the target package. Use when starting a new coding task, before writing any code, switching to a different package, or needing to refresh project conventions and standards.

Understand and customize the local Trellis architecture inside a user project. Use when modifying .trellis plus platform hooks, settings, agents, skills, commands, prompts, workflows, the channel runtime (trellis channel), bundled runtime agents under .trellis/agents/, selectable workflow templates, registry-backed spec refresh, cross-session memory (trellis mem) generated by trellis init, or AI-facing bundled skills (trellis-channel, trellis-session-insight, trellis-spec-bootstrap) and bundled-skill auto-dispatch flow.

Use this skill to run an Ebitengine app (anything implementing ebiten.Game) headlessly and programmatically — no visible window and no changes to the app's source — to test, debug, screenshot, or otherwise exercise it. It launches the app as a vmhost guest, drives it through its ticks faster than real time, injects keyboard / mouse / touch / gamepad / text input, reads the rendered frame back as pixels to assert on or dump as a PNG, and observes the audio the app plays. Use when you need input injection, deterministic multi-tick runs, golden-image checks, or audio assertions — for example to reproduce an input-dependent bug, verify behavior after a sequence of clicks or key presses, check that the right sound plays, capture a single rendered frame, or let an AI agent drive an app end-to-end.