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概要と使いどころ
Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.
日本語の概要は準備中です。原文の説明を表示しています。
Music-video production skill for turning an existing MP3 into a pure-visual (no-voiceover) music video through a bundled Busabase App-in-Skill UI. Use when the user wants to build an MV from a song they upload: write a one-line concept, keep a cast of characters with reference cards, and break the song into storyboard shots where each shot has a scene description and an image + video that can be either AI-generated (image-to-image from the character cards, draft video via local LTX) or uploaded by the user. Same character/storyboard management model as kelly-drama. Song generation (creating the song itself, incl. voice-cloned singing) is a documented future capability.
日本語の概要は準備中です。原文の説明を表示しています。
Run a Busabase-backed sales prospecting and first-touch outreach desk with a bundled Hono App-in-Skill deployable to AirApp. Use whenever the user invokes $kelly-sales-outreach or /kelly-sales-outreach, wants to enter a product or service and discover its ideal customers, asks to continuously find target accounts or public business contacts, needs evidence-based ICP scoring and personalized outreach drafts, or wants to review and send cold outreach safely. Four subcommands - setup, profile, research, send - plus the desk itself. Every first-touch message requires human approval; one company receives one initial email; guessed addresses, purchased lists, secret exposure, automated spam, and unreviewed external sends are forbidden.
日本語の概要は準備中です。原文の説明を表示しています。
Short-drama and product-video development skill for planning drama projects through a bundled Busabase App-in-Skill UI, with each project linked to a specified HyperFrame project and each episode linked to a HyperFrame composition for final motion/video tuning. Use when the user asks to create, edit, organize, or review short-drama scripts, product explainer episodes, character consistency cards, series bibles, episode beat sheets, storyboard shots, HyperFrame project/composition links, production checklists, or a drama planning workbench.
日本語の概要は準備中です。原文の説明を表示しています。
Run a Busabase-backed job-search outreach desk with a bundled Hono App-in-Skill deployable to AirApp. Use when the user invokes $kelly-jobhunt or /kelly-jobhunt, wants to turn a resume into a structured profile and a typeset PDF, wants to find target companies and their contact addresses instead of applying through a job board, wants one tailored application email drafted per company, or wants to review and approve each send. Four subcommands - setup, profile, research, send - plus the desk itself. One company receives exactly one email; mailbox credentials live in the Busabase Vault and never reach browser code.
日本語の概要は準備中です。原文の説明を表示しています。
Build and operate a Busabase-backed mainland China A-share strategy experiment desk with a bundled local Hono App-in-Skill, evidence-backed strategy research, strategy-level L1/L2/L3 manual maturity approvals, one CNY virtual ledger per strategy, and dated strategy backtest reports. Use when the user invokes $kelly-invest-stock or /kelly-invest-stock, wants to define or compare A-share strategies, inspect a strategy and its virtual book, record research evidence, manually approve strategy maturity, or review dated backtests, drawdown, and contribution to the total book. It never connects to a brokerage, places orders, moves money, or presents generated analysis as personalized investment advice.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
日本語の概要は準備中です。原文の説明を表示しています。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
日本語の概要は準備中です。原文の説明を表示しています。
Compile LaTeX and TeX documents with the bundled Tectonic executable.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.
日本語の概要は準備中です。原文の説明を表示しています。
Best practices for agents managing PostHog skills via the MCP `llma-skill-*` tools — how to discover, read, create, update, and refactor skills efficiently, especially large skills with many bundled files. Use whenever you are about to call any `llma-skill-*` tool, asked to author or edit a shared skill, or troubleshoot why a skill write was rejected. Pairs with `skills-store` (which covers the raw tool surface) by adding the decision-tree, efficiency, and pitfall guidance.
日本語の概要は準備中です。原文の説明を表示しています。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
日本語の概要は準備中です。原文の説明を表示しています。
Focused Signals scout for PostHog projects using AI observability. Rotates through a set of lenses — cost, latency, errors, volume, eval performance, eval/enrichment config, clusters, and tool usage — watching each for trends and spikes sliced by the dimensions it discovers over time. Leans on the sandbox's bundled `exploring-llm-*` deep-dive skills for the actual queries. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other scouts.
日本語の概要は準備中です。原文の説明を表示しています。
Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
日本語の概要は準備中です。原文の説明を表示しています。
Help users debug PostHog Error Tracking stack-trace symbolication for any supported platform — JavaScript/TypeScript web, React Native (Hermes), Android (Proguard / R8), or iOS / macOS (dSYM). The PostHog symbol-set lookup flow is universal across platforms; build-tool and artifact details live in per-platform references (JavaScript is fleshed out, others come as we encounter them). Use when stack frames stay minified or obfuscated after symbols are uploaded, PostHog symbol sets show last_used but frames are not readable, chunk IDs or dSYM UUIDs do not match, "Token not found" appears, uploaded source maps / dSYMs / Proguard mappings look empty, or bundler / symbol-upload configuration needs troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and optimize application performance, including bundle size, rendering, database queries, and Core Web Vitals.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.
日本語の概要は準備中です。原文の説明を表示しています。
Install or update ToolUniverse in Claude Science — create the conda env, install the tooluniverse pip package, and (re)build the tooluniverse-research skill by fetching the current workflow library from GitHub. Use for first-time setup, upgrading the ToolUniverse version, refreshing the bundled workflows after an upstream release, or reinstalling on a new machine.
日本語の概要は準備中です。原文の説明を表示しています。
Stellar standards, ecosystem, and reference. Covers SEPs (Stellar Ecosystem Proposals), CAPs (Core Advancement Proposals), and a quick map for picking the right standard for wallets, anchors, payments, deposits/withdrawals, federation, deep links, and KYC. Also bundles ecosystem references (DeFi protocols, dev tools, wallets, infra, community projects), curated documentation links, and MCP servers (live tools such as Raven). Use when you need to know which SEP applies, or want a starting point for ecosystem integrations, official docs, or live MCP tooling.
日本語の概要は準備中です。原文の説明を表示しています。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
日本語の概要は準備中です。原文の説明を表示しています。
Inspects and visualises a ROS-2-enabled CARLA server from Docker containers, with no local ROS 2 install — list/echo/rate-check topics (the "are the topics actually on the wire" check), run the bundled map-and-lidar demo stack (hero vehicle with camera/lidar/GNSS/IMU on autopilot), and open RViz2 with the lane-network preset. Use when the user asks to "check the ROS topics", "echo /carla/map", "is CARLA publishing to ROS", "visualise the lidar in RViz", "open rviz", or "run the ROS 2 demo".
日本語の概要は準備中です。原文の説明を表示しています。