Prepare and execute a Plannotator release — draft release notes with full contributor credit, bump versions across all package files, build in dependency order, and kick off the tag-driven release pipeline. Use this skill whenever the user mentions preparing a release, bumping versions, writing release notes, tagging a release, or publishing. Also trigger when the user says things like "let's ship", "prep a release", "what's changed since last release", or "time to cut a new version".

Invoke whenever writing, changing, reviewing, or sweeping Vitest tests in this repo. Authoring gate for new or changed tests, plus an audit workflow for low-value, implementation-coupled, or duplicative tests and the test-only production seams they keep alive. Use when asked to add tests, review tests, clean up tests, or when a PR touches *.test.ts.

Cut an OpenSEO release — bump the version, draft user-facing release notes from commits since the last tag, run a review + subagent-verification pass, and open a "release: vX.X.X" PR. Use when the user asks to prepare a release, bump the version, or write release notes.

Find all FIXME comments across the codebase and fully implement the work they describe. Use when the user asks to fix, resolve, or address FIXME comments, or when running the "fixme" command. Runs a discovery script to find every FIXME, expands multiline comment blocks, groups related FIXMEs across files into a single implementation task, completes the full underlying code changes, removes the FIXME comments only after the work is done, and verifies that no FIXMEs remain.

Covers the PreviewUrlModifier extension point in Website Builder. Use when a user wants to inject custom query parameters into live preview URLs — e.g. signed tokens, tenant identifiers, feature flags — from their Webiny project. Handles the full registration pattern: implementing the interface, wiring via createFeature + RegisterFeature, and registering via webiny.config.tsx. Supports async modifier methods (e.g. remote token fetch).

The Xberg workspace layout — the version source of truth (root Cargo.toml [workspace.package] version), the 19 workspace members and 3 excluded crates, the distribution packages under packages/, the tools/ directory, and the ignore-file allowlists a new workspace member must be added to. Load when navigating the repo, deciding where code belongs, or wiring a new crate or binding package.

Audit Xberg before a push or release by reconciling CI, Publish Release dry-run, Benchmarks, generated freshness, changelog, and remote branch state. Load for release-readiness work, not routine local commits.

Use Desktop Commander for terminal and command-line work, especially anything that needs a shell whose state persists across turns: Python/Node REPLs, database shells, dev servers and other long-running processes, SSH into remote machines, and Windows PowerShell. Also handles everyday terminal tasks — navigating folders, choosing the right command for the user's shell (PowerShell, cmd, bash, zsh), running Docker/curl/cloud-CLI commands, inspecting processes and ports, and saving recurring workflows as scripts — even when the user doesn't say "Desktop Commander" or "terminal." Reach for it on pasted errors like "command not found", "permission denied", "EADDRINUSE", "address already in use", "npm ERR!", "ModuleNotFoundError", or "ENOENT", and on intents like "what's using port 3000", "kill that process", "ssh into my server", or "why won't my dev server start". Works on Windows, macOS, and Linux. Never run destructive commands without explicit confirmation.

Use Desktop Commander for terminal and command-line work, especially anything that needs a shell whose state persists across turns: Python/Node REPLs, database shells, dev servers and other long-running processes, SSH into remote machines, and Windows PowerShell. Also handles everyday terminal tasks — navigating folders, choosing the right command for the user's shell (PowerShell, cmd, bash, zsh), running Docker/curl/cloud-CLI commands, inspecting processes and ports, and saving recurring workflows as scripts — even when the user doesn't say "Desktop Commander" or "terminal." Reach for it on pasted errors like "command not found", "permission denied", "EADDRINUSE", "address already in use", "npm ERR!", "ModuleNotFoundError", or "ENOENT", and on intents like "what's using port 3000", "kill that process", "ssh into my server", or "why won't my dev server start". Works on Windows, macOS, and Linux. Never run destructive commands without explicit confirmation.

Create and maintain a Markdown knowledge base that any AI agent can read, search, and update. Use when the user wants to start a knowledge base, add or update notes, organize docs/notes for an agent or LLM to consume, build an index of notes, or run a cleanup/maintenance pass on an existing MD knowledge base. Triggers include "knowledge base", "KB", "notes for the agent", "index of notes", "second brain", "docs for AI context", "add a note", "update the KB".

Create and maintain a Markdown knowledge base that any AI agent can read, search, and update. Use when the user wants to start a knowledge base, add or update notes, organize docs/notes for an agent or LLM to consume, build an index of notes, or run a cleanup/maintenance pass on an existing MD knowledge base. Triggers include "knowledge base", "KB", "notes for the agent", "index of notes", "second brain", "docs for AI context", "add a note", "update the KB".

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.

Open gaps tracker — what's flagged and not yet closed. Use when the user asks "what gaps are open", "gap tracker", "remediation status", or wants to close (--close GAP-ID) or risk-accept (--accept GAP-ID) a tracked gap.

Run a focused N-question study session on a subject — MBE, essay, or flashcards. Tracks performance and updates the study plan. Use when the user says "run me 10 questions on [subject]", "do a session on [subject]", "let's do 5 cards on [subject]", or wants to drill a fixed number of questions and have the plan adapt.

Add data to an open investigation — documents, interview notes, or observations. Processes batches against the documented pull criteria, surfaces significant items, and logs everything reviewed for coverage verification. Use when new evidence, interview notes, or document productions come in for an open investigation.

Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.

Tabular review — one row per document, one column per data point, every cell cited to source. Built for M&A diligence ("review these 200 target contracts for change-of-control, assignment, and MAC clauses") but works for any batch review that needs a spreadsheet out the other end. Use when user says "tabular review", "review grid", "build a grid", "extract these fields from these contracts", "review these documents for X, Y, Z", "give me a spreadsheet of", "batch review", or points at a folder of documents and asks to compare them.

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.

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.