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Read-only view of the execution roadmap. Lists every epic with its per-status tallies, nests the still-open rows beneath their epic, collapses wholly-finished epics into one rollup line, and closes by naming the next planned task in file order. Four verbs — `list` (the reader's view), `tasks` and `epics` (filterable row and epic queries), `next` (the first planned task). Invoke any time to see what is left to build. Not a workflow phase; never writes; never starts or commits work.

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Review the auto-extracted candidates in `.claude/memory/_pending.md` and commit keepers to the canonical memory files (`landmarks.md`, `libraries.md`, `decisions.md`, `landmines.md`, `conventions.md`, `pending-questions.md`, `backlog.md`). Invoke at session start when the SessionStart hook reports pending candidates, or any time `_pending.md` has accumulated entries you want to curate. Reset the pending body after flushing.

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EXPERIMENTAL. Join or leave an org-team coordination channel as a session peer. `/companion on <channel_id> [peer_id]` registers this Claude Code session as a `pclass:"session"` peer and works the claim → execute → signal loop, escalating un-decidable forks to the lead (never deciding). `/companion off` deregisters. `/companion status` reports the current peer. Part of org mode (Article X), which is opt-in via `velocity.org_mode.enabled` and OFF by default. Use when a human launches a second session to act as a peer alongside a lead.

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Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.

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Geração de demanda multi-canal, otimização de mídia paga, estratégia de SEO e programas de parcerias para startups Series A+. Inclui calculadora de CAC, playbooks de canal, integração com HubSpot e táticas de expansão internacional. Use ao planejar campanhas de geração de demanda, otimizar mídia paga, construir estratégias de SEO, estabelecer parcerias, ou quando o usuário menciona demand gen, anúncios pagos, LinkedIn ads, Google ads, CAC, aquisição, geração de leads ou pipeline.

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Renderize diagramas SVG limpos em estilo blueprint a partir de specs JSON. Use quando usuários pedirem para desenhar, esboçar ou diagramar um fluxo de requisição, rede neural, bloco transformer, arquitetura de sistema, máquina de estados, pipeline de dados, ou qualquer visual técnico de nós e arestas que queiram como SVG para docs, READMEs, posts ou slides.

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Aplica as cores oficiais da marca Anthropic e tipografia a qualquer tipo de artefato que possa se beneficiar da identidade visual da Anthropic. Use quando diretrizes de cores da marca ou padrões de design, formatação visual ou padrões de design corporativo se aplicarem.

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Aplica as cores de marca oficiais e tipografia da Anthropic a qualquer tipo de artefato que possa se beneficiar de um visual e sentimento do padrão Anthropic. Use quando diretrizes de cores de marca, guias de estilo, normas de design visual ou padrões de design corporativo se aplicarem.

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Geração de imagens com tecnologia de ponta usando Stable Diffusion via HuggingFace Diffusers. Use quando gerar imagens a partir de prompts em texto, realizar tradução de imagem para imagem, inpainting ou construir pipelines de difusão customizados.

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Híbrido RNN+Transformer com inferência O(n). Tempo linear, contexto infinito, sem cache KV. Treina como GPT (paralelo), infere como RNN (sequencial). Projeto Linux Foundation AI. Produção em Windows, Office, NeMo. RWKV-7 (março de 2025). Modelos até 14B parâmetros.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.

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Applies Canvas brand colors, typography, and visual polish to artifacts that benefit from a consistent product look-and-feel. Use it when brand colors, style guidelines, visual formatting, or company design standards apply.

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Research-grounded typography for any text-bearing surface — web, PDF, email, documents. GENERATIVE: apply correct line length, font sizing, contrast, dark mode, mobile, and Japanese rules from the start when building any UI, page, layout, or document. REVIEW: audit existing CSS/HTML/templates for typography violations. Use this skill whenever you are writing CSS that touches font-size, line-height, max-width, color, or font-family. Use it when building any frontend component, page, or layout — even if the user doesn't mention typography. Use it when reviewing any rendered output for readability. Use it when generating PDFs, emails, or any formatted document. If text will be read by a human, this skill applies.

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Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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。

UI/UX design intelligence for web, mobile, and desktop. This skill should be used when designing, building, reviewing, or fixing interfaces, including pages, components, design systems, accessibility, interaction, responsive layout, typography, color, charts, and stack-specific UI implementation. Searchable local data: 79 searchable styles (50 active), 192 product palettes and reasoning profiles, 74 font pairings, 119 UX guidelines, 105 icons, 17 GSAP presets, 25 chart types, and 22 stacks.

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Investigate supply chain attack artifacts including trojanized software updates, compromised build pipelines, and sideloaded dependencies to identify intrusion vectors and scope of compromise.

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Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

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。

Professional data visualization skill specialized in creating reader-friendly, accessible, and aesthetically pleasing charts and dashboards. Use this skill when you need to create visualizations, choose appropriate chart types, design color schemes, create dashboards, or apply design best practices for data communication. Expertise includes visualization principles, color theory, typography, layout design, and accessibility guidelines.

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Use when the user starts a non-trivial coding/building task in Claude Code (e.g. "帮我做个 X", "help me build Y", "add feature Z", "搭一个 Z"). Layers an AI-native pedagogical coat over normal coding work — surfaces implicit assumptions in user intent, annotates non-trivial design decisions inline with brief concept-scaffolding for terms the user may not know, and replaces "should I run it?" handoffs with rigorous AI self-verification plus a clean list of what only the user can verify. Skip for pure Q&A, single-line edits, read-only investigation, or refactoring along an existing pattern.

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Detects deeply nested loops with duplicated inline logic and recommends extracting into small, named functions. Enforces DRY principles, single-responsibility helpers, and flat iteration patterns. Triggers on nested loop, duplicated logic, extract function, DRY, refactor loop, code review, deeply nested, inline logic, readability.

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Builds a DRAFT stakeholder map for one project from the charter, organisation chart, RACI, governance terms, steering minutes, meeting notes and messages the user provides: a stakeholder register, interest and influence ratings each with its stated basis, stance as evidenced by a verbatim fragment with source and date, gaps, and a proposed engagement plan per stakeholder, every line traced to a source and every missing fact marked UNKNOWN. Use when the user asks to "build the stakeholder map", "who are the stakeholders on this project", "map interest and influence", "where does each stakeholder stand", "draft the stakeholder engagement plan" or "refresh the stakeholder register". Do not use for a customer account's stakeholders, use account-plan-builder instead; for a one-page brief before one meeting, use meeting-prep-onepager; for logging decisions and risks against the project, use project-status-tracker. Drafts for human review; never approves, authorises or signs off.

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Prepares a DRAFT data incident impact brief from the incident notes and data inventory the user provides: the timeline as stated, systems involved matched to the inventory, data categories held and evidenced as involved, people and record counts potentially affected with the source and method behind every figure, third parties, notification questions for legal and the data protection officer, and every UNKNOWN with the evidence that would settle it. No legal determination: it never states whether the incident is a reportable breach, whether notice is due, to whom or by when. Use when the user asks to "prepare the impact brief for this incident", "what data was involved", "how many people are affected", "map the incident to our data inventory" or "pull together what legal needs on the incident". Do not use for the blameless postmortem or root-cause timeline, use incident-postmortem-drafter instead. Drafts for human review; never approves, authorises or signs off.

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Reads a contract list (spreadsheet, CSV or pasted table) and returns a DRAFT renewal radar: each active contract's end date, notice deadline with its arithmetic, days remaining, renewal type, owner and the decision it needs as options, sorted by urgency, with owner action lists and a deadline calendar. Never renews, terminates, serves notice or recommends. Use when the user asks to "build a renewal tracker", "tell me what comes up for renewal", "work out when notice is due", "show me which contracts auto-renew" or "run the quarterly contract review". Do not use for reviewing one contract's full text or clauses, use contract-review-pack instead. Drafts for human review; never approves, authorises or signs off.

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。