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Make an original animated music video (MV / PV) for a song from its audio file plus lyrics, in the Vocaloid / J-pop lyric-video tradition: 手書き/tegaki (V家手书, 手描きMV, ボカロPV), 文字PV and kinetic typography (リリックモーション, リリックビデオ, 歌詞動画, 歌词视频, 动态歌词), anime-illustration MVs with an original character — for any mood, from dark ballads to bright upbeat summer pop. Renders to MP4 with a deterministic Canvas2D/WebGL2 engine in a headless browser: JIZURA lyric motion, AI-generated art, optional Blender toon 3D, beat-locked editing, 9:16 vertical/Shorts version. Use it whenever the user wants a music video, MV, PV, lyric video or beat-synced animation for a song, e.g. "make an MV for my song", "turn this track into a lyric video", "给这首歌做个MV/PV", "做个V家手书", "この曲のMVを作って", "ボカロPV作って", "縦型ショートも" — even if they only give a .wav/.mp3 and say "make a cool video for it". Not for editing existing footage or clip compilations.

This skill should be used when the user asks to "cut to the beat", "fix the pacing", "edit this sequence", "sync cuts to music/BPM", "add a match cut / J-cut / L-cut", "build a speed ramp", or "plan a shot sequence / edit timeline". Covers editing rhythm, transitions, retiming, and translating an edit plan to an NLE or a code timeline (Remotion/AE frames).

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Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or Cypher migration — use neo4j-migration-skill. Does NOT cover CLI/admin tasks on an existing DB — use neo4j-cli-tools-skill.

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Expert copywriter and copy editor combining David Ogilvy's clarity, Ann Handley's warmth, and modern conversion science. Full copy lifecycle: writing new copy and editing existing copy. Grounded in positioning-first thinking, voice-of-customer research, and the Seven Sweeps editing framework. Use when writing or editing any copy: copywriting, write copy, headlines, taglines, email copy, ad copy, landing page copy, product copy, UX writing, CTAs, value proposition, microcopy, sales copy, conversion copy, SaaS copy, startup copy, positioning, messaging, voice-of-customer, landing page, copy editing, edit copy, review copy, proofread, polish copy, tighten copy, copy sweep, copy feedback, sharpen messaging.

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Product Hunt launch optimization with specific specs, timing, and gallery strategy. Covers taglines, gallery images, maker comments, and launch day tactics. Use for: product launches, startup launches, side project launches, Product Hunt optimization. Triggers: product hunt, ph launch, product hunt launch, launch strategy, product launch, startup launch, product hunt tips, product hunt gallery, ph optimization, launch day, product hunt maker

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UI/UX design intelligence for web and mobile. Includes 50+ styles, 161 color palettes, 57 font pairings, 161 product types, 99 UX guidelines, and 25 chart types across 10 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, and HTML/CSS). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, and check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, and mobile app. Elements: button, modal, navbar, sidebar, card, table, form, and chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, and flat design. Topics: color systems, accessibility, animation, layout, typography, font pairing, spacing, interaction states, shadow, and gradient. Integrations: shadcn/ui MCP for component search and examples.

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Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

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Advanced and operational chat.agent capabilities for Trigger.dev, loaded on demand. Load this when working on the raw Sessions primitive (sessions / SessionHandle), a custom chat transport or the realtime wire protocol, durable sub-agents (AgentChat, chat.stream.writer), human-in-the-loop, steering, actions, background injection (chat.defer / chat.inject), fast starts (preload, Head Start via @trigger.dev/sdk/chat-server), context resilience (compaction, recovery boot, OOM, large payloads), chat.local run-scoped state, offline testing with mockChatAgent, or prerelease/version upgrades. For the everyday chat.agent({...}) definition and the useTriggerChatTransport happy path, use the trigger-authoring-chat-agent skill instead.

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🇺🇸 B2B SaaS Growth — PLG vs SLG Playbook — Diagnose whether your problem is distribution, pricing, or PMF. PLG/SLG selection by ACV and sales cycle, the 5-stage path from $0 to $10M ARR, NRR discipline, affiliate & channel motion, enterprise tiering. Built from HeyGen, Deel, Vercel, Supabase, Snowflake patterns. 🇨🇳 B2B SaaS 增长 — PLG vs SLG 决策手册 — 诊断你的瓶颈是分发、定价还是 PMF。按 ACV 和销售周期选 PLG/SLG，$0 → $10M ARR 的 5 阶段路径，NRR 纪律，联盟与渠道打法，Enterprise 分层。提炼自 HeyGen、Deel、Vercel、Supabase、Snowflake。 🇯🇵 B2B SaaS グロース — PLG vs SLG プレイブック — 課題が流通・価格・PMFのどれかを診断。ACVとセールスサイクルでPLG/SLG選定、$0→$10M ARRの5段階パス、NRR規律、アフィリエイト＆チャネル。 🇰🇷 B2B SaaS 성장 — PLG vs SLG 플레이북 — 병목이 유통·가격·PMF 중 무엇인지 진단. ACV와 세일즈 사이클로 PLG/SLG 선택, $0 → $10M ARR 5단계 경로, NRR 규율, 제휴 & 채널. Triggers: "B2B growth" | "B2B SaaS" | "PLG" | "SLG" | "product-led growth" | "sales-led growth" | "PLG or SLG" | "SaaS pricing" | "ARR growth" | "NRR" | "go to market" | "enterprise sales" | "affiliate" | "PMF" | "B2B 增长" | "PLG 还是 SLG" | "SaaS 定价" | "渠道合作" | "PLG/SLG 선택"

Build and grow developer and user communities from scratch — covering ambassador programs, community-led growth (CLG), event operations, content strategy, and health metrics. Grounded in real playbooks from Notion, Lovable, AFFiNE, ClickUp, Asana, and Lark. Use when: building a community for an open-source project, SaaS product, or brand; recruiting and managing ambassadors or community leaders; planning community events (online/offline meetups, AMAs, hackathons); designing ambassador tier/points systems; setting community health KPIs; launching a Build-in-Public strategy; setting up Discord, Slack, or Lark communities; running developer relations (DevRel); or when the user asks about community operations, ambassador programs, CLG, user advocacy, KOC/KOL cultivation, UGC flywheels, community monetization, or any variation of "how do I grow my community".

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Maintain the durable, per-ticket run log that warp deploy/update skills append to at every milestone — a Linear-document-by-title primary with single-writer discipline and a local-file fallback, a machine-parseable per-chain row plus prose entry shape, and a surface-the-URL-as-proof hard gate. Referenced by any warp deploy/update skill that must leave a retrospective trail across worker restarts.

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Quarterly Business Review template generator — takes account info (ARR tier, adoption metrics, goals) and produces a complete QBR deck outline and talking points. Use when asked to "prepare a QBR", "build a quarterly review", "write our QBR agenda", or "create QBR talking points for this account".

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Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.

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Map design, spatial analysis, and Geographic Information Systems. Covers map projections and their distortion trade-offs, coordinate systems, thematic mapping techniques (choropleth, proportional symbol, dot density, isoline), remote sensing and satellite imagery, GIS data models (vector and raster), spatial analysis operations (overlay, buffer, interpolation, network analysis), and cartographic design principles. Use when creating maps, analyzing spatial data, selecting projections, interpreting satellite imagery, or reasoning about spatial relationships in any domain.

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Being a constructive participant in networked communities. Covers digital footprint management, online etiquette and professional communication, collaborative tools, attribution and copyright, cyberbullying recognition and response, and the responsibilities that come with amplification. Use when helping learners think about how to behave online -- not what is technically allowed, but what is actually healthy for themselves and their communities.

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Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.

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Set up Hierarchical Agent Memory (HAM) — scoped CLAUDE.md files per directory that reduce token spend. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "ham sync".

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Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".

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Apply brand colors and typography to artifacts. Use when brand colors, style guidelines, visual formatting, or company design standards apply. Ensures consistency across branded content.

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Category Theory Tripartite: SICP generic dispatch, CTP Racket categories, CatColab double theories across 3 parallel worldlines with GF(3) conservation

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Mermaid diagram syntax reference for all diagram types — flowchart, sequence, class, state, ER, gantt, git graph, mindmap, timeline, user journey, pie, quadrant, XY chart, block, sankey, C4, kanban, and more. Use when constructing or debugging any Mermaid diagram definition.

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Executes SAM Stage 5 — opens an attempt on a single ARTIFACT:TASK, dispatches it to a fresh stateless agent session, runs quality gates, and records an ARTIFACT:EXECUTION with implementation results and verification output on the task. Use when Stage 4 Task Decomposition is complete and tasks are ready for execution, when re-executing a task after Stage 6 returns NEEDS_WORK, or when dispatching a task to a language-appropriate specialist agent via the development harness pipeline.

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Analyze git repository history to produce a codebase risk profile before code review. Probes repo size, runs 7 parallel git analysis pipelines (hotspots, bug magnets, bus factor, contributor momentum, firefighting frequency, newly added files), cross-references hotspots with bug magnets to identify high-risk files with primary owner annotation, and writes walkthrough/recon-report.md. Use before code review to prioritize reviewer attention, at the start of linear-walkthrough Phase 1 Discovery to weight file coverage, or when onboarding to an unfamiliar codebase.

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Run a quick Deepline demo with a maintained Play.

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