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概要と使いどころ
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
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Prepares meeting materials by gathering context from Notion, enriching with Claude research, and creating both an internal pre-read and external agenda saved to Notion. Helps you arrive prepared with comprehensive background and structured meeting docs.
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。
Create interface designs, wireframes, and design systems. Masters user research, accessibility standards, and modern design tools. Specializes in design tokens, component libraries, and inclusive design. Use PROACTIVELY for design systems, user flows, or interface optimization.
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 tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
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。
郑希观点库——基于易方达基金经理郑希 2012–2026 年全部公开观点原文语料，外加从语料蒸馏、有本人原话佐证的郑希投资方法的可溯源 research skill。能做： (1) 溯源问答——他怎么看 AI算力/光通信/新能源/半导体/ROE 等，引用其原话作答； (2) 讲解他的投资方法/框架/选股逻辑；(3) 前瞻应用——用他的方法分析当下任意主题/行业/个股，语料没谈过也能据框架推演； (4) 风格化点评——用他季报/手记的口吻写市场点评、季度展望； (5) 言行对照——用他全部基金真实数据（季度持仓/净值/业绩/规模/任职回报）核对"说的"与"买的"，或答他的业绩/重仓/规模； (6) 全市场查询对比——内置约 2.7 万只基金列表，按需抓任意基金真实数据做查询或与郑希对比； (7) 郑希框架评分——给一只基金按他的方法打分（多像郑希会买的）。 When the user mentions 郑希/易方达郑希/zhengxi, asks his view on a sector/stock/theme, his 投资方法/框架/选股/风格/持仓/业绩/净值/规模, wants to apply his approach, a commentary 用郑希口吻, to check words vs holdings, or to look up/compare/score ANY China mutual fund (任意基金/某基金经理/同类对比/给基金打分)——use this skill, even if they don't say "skill", even if the topic isn't in his corpus (fall back to his method). 引用忠于原文、不杜撰；推演与原话区分。研究学习辅助，非投资建议。
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。
Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.
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 gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
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。
Turn a research paper into teaching materials — a lecture outline, the 3-5 results worth presenting (with intuition), a slide skeleton ready for `/create-lecture`, discussion questions, and a problem-set brief. Reads the paper end-to-end and pitches to a stated audience level. Use when user says "turn this paper into a lecture", "teach from this paper", "build slides from this PDF", "make teaching materials from X", "I'm presenting this paper to my class".
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。
Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an `/interview-me` spec, delegates the data-management plan to `/data-management-plan` and the facilities statement to `/capture-environment`, and emits a funder-requirements checklist. Use when user says "draft a grant", "write a proposal", "NSF proposal", "NIH aims", "ERC application", "foundation grant", "specific aims", or "scaffold a grant proposal". NOT a submission tool — produces a draft the user uploads to the sponsor's portal themselves.
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。
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when user says "preregister", "draft a preregistration", "OSF preregistration", "AsPredicted", "AEA RCT registry", "PAP", "preanalysis plan", or before launching an experiment / data collection / analysis on data the analyst has not yet seen. NOT a registry submission tool — produces a document the user uploads to OSF / AsPredicted / AEA themselves.
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。
Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate looks wrong", or "it worked yesterday". Tuned for research code (R/Stata/Python): type coercion, NA/merge blow-ups, factor levels, clustering/SE choices, weighting, collinearity/convergence, seeds, package-version drift. Use `--no-fix` to localize the root cause without editing shared or load-bearing files.
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。
How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.
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。
ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.
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 types /hep-storm, mentions @Hephaestus storm, or asks to force-robustly drive a loop-worthy goal (apps, sites, agents, automations, debugging, multi-step research, data/report generation) to a verified finish. Stormbreaker routes the goal to real Agentlas specialists, materializes a dependency-ordered pipeline fabric, and runs a verifier-first loop that does not stall, run away, or claim false success. Trivial questions are answered directly, not stormed.
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 types /hep-storm or /agentlas-storm, says @Hephaestus storm <goal>, or asks to drive a goal to verified completion through a force-robust Stormbreaker loop. Stormbreaker routes the goal to real Agentlas specialists, materializes a dependency-ordered pipeline fabric, and runs each work packet as a verifier-first hardened loop that does not stall, run away, or claim false success. Use it for loop-worthy work — apps, sites, agents, automations, debugging, multi-step research, data/report generation. Trivial questions are answered directly, not stormed.
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 generating a single installable agent that should keep learning, track sources, refresh research, propose repairs, or improve itself over time without becoming a multi-agent team.
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。
Find what is actually working in the user's niche on YouTube and rank it by how far each video beat its own channel, then name the formula. Use for "what's working right now", "find viral videos in my niche", "why did this blow up", competitor research, or a swipe file.
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。
Policy-safe relationship rankings, commitments, aliases, person profiles, and topic research. Always use Minutes' bounded native CLI surfaces; never build or read a durable graph cache.
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。
Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.
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。
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitor_only, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
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。
Breaks down complex software, writing, or research tasks into small, atomic, independently completable units with dependency graphs and milestone breakdowns. Use when the user asks to plan a project, decompose a feature, create subtasks, split up work, or needs help organizing a large piece of work into a step-by-step plan. Triggered by phrases like "break down", "decompose", "where do I start", "too big", "split into tasks", "work breakdown", or "task list".
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 one independent Council critique against engagement-report research evidence. Use as the manual separate-session fallback when independent critic agents are unavailable.
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。
Create, update or review presentation-first HTML slide decks about HVE Core under slides/. Use for slide content research, deck styling, VS Code-style walkthroughs, presenter controls, or single-file HTML sharing in this repository.
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。
Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or compare candidate changes in an evo workspace. The orchestrator plans and spawns optimization subagents; candidate edits/runs belong to those subagents. Width is set via subagents=N (1 for serial workloads, larger for parallel); the loop's structural value applies at any width.
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。