調査、論文、教育、資格学習（4,124 件）
概要と使いどころ
Identify 3-5 potential customer segments with demographics, JTBD, and product fit analysis. Use when exploring market segments, identifying target audiences, evaluating new markets, or learning how to segment a market.
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Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.
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。
Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, Mermaid (rendered to PNG via headless Chrome), PlantUML, footnotes, alerts, infographics, and optional bottom citations for external links. Use when user asks for "markdown to html", "convert md to html", "md 转 html", "微信外链转底部引用", or needs styled HTML output from markdown.
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。
Concept prototype before GDDs — throwaway HTML, Engine or Paper build, PROCEED/PIVOT/KILL/NOT ASSESSED. After /brainstorm and /setup-engine.
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。
Log genuine, recurring repository friction to .agents/PAPERCUTS.md — confusing setup, a flaky repo command or script, a misleading in-repo error, stale generated files, or a non-obvious gotcha that will cost the next contributor time. Also use to review, deduplicate, and resolve existing entries. Gate hard before logging: only friction the repository itself can fix counts. Never log the agent's own sandbox/permission errors, shell-scripting mistakes, transient flakiness, or third-party tool quirks the repo can't change.
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。
Operation-aware node configuration guidance. Use when configuring nodes, understanding property dependencies, determining required fields, choosing between get_node detail levels, or learning common configuration patterns by node type. Always use this skill when setting up node parameters — it explains which fields are required for each operation, how displayOptions control field visibility, and when to use patchNodeField for surgical edits vs full node updates.
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 verified correction or repeated workflow into a reusable local learning.
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。
Synthesize repeated themes from selected research conversations or notes.
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。
The project's episodic memory: a timestamped ledger of rules paid for with real outages and near-misses, one entry per incident. Load WHENEVER you write or review a DB migration or schema change, touch deploy/release workflows (.github/workflows/deploy-*, promote.yml, vercel config), plan a promote/release, respond to a prod incident, or when another skill references a learning. ALSO load after resolving any incident or near-miss: it must leave a new ledger entry in the same session.
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。
Runbook for turning a new HubSpot lead into a researched, personalized follow-up email and a proposed call slot for {{projectName}}. Covers company research, the sales playbook (positioning, tone, qualifying questions), HubSpot record updates, Google Calendar availability, and the human approval gate before anything sends.
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。
Runbook for turning a new HubSpot lead in {{hubspot_list}} into a researched, personalized outbound sequence for {{projectName}}. Covers enrichment research, the outreach playbook (angles, proof points, what to avoid), sequence drafting, HubSpot logging, the daily cap, and the human approval gate before anything sends.
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。
Get a complete picture of any company or person before outreach. This skill always works with web search, and gets significantly better with enrichment and CRM data.
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。
Deep research agent skill. Use when the user needs thorough, scientific, truth-seeking research on any topic -- investigating claims, finding primary sources, synthesizing evidence, producing cited reports. Triggers on: 'research this', 'investigate', 'deep dive', 'find sources', 'what does the evidence say', 'literature review', 'fact check', 'analyze the research on', any request requiring multi-source investigation with citations.
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。
Free domain research and availability checking. No API keys or credentials required. Uses RDAP (1195+ TLDs) with whois CLI fallback for universal coverage. Checks if domains are available, searches keywords across TLDs, performs WHOIS/RDAP lookups, checks expiry dates, and finds nameservers. Use when the agent needs to: check if a domain is available, search for domains, find who owns a domain, check domain expiration, get nameservers, bulk check domains, or do any domain research. Triggers on: 'check domain', 'is domain available', 'search domains', 'domain availability', 'who owns this domain', 'whois', 'domain expiry', 'when does domain expire', 'nameservers for', 'domain research', 'find domains for', 'domain ideas', 'bulk domain 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。
Research first, then draft. This skill never sends generic outreach - it always researches the prospect first to personalize the message. Works standalone with web search, super...
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, authenticate, troubleshoot, and operate Gemini Notebook through the notebooklm-py CLI or typed async Python API. Use for notebook and source management, grounded chat and research, and artifact generation or download when the user mentions Gemini Notebook, notebooklm-py, the notebooklm CLI, or its Python API. Do not use for the generic Gemini API or unrelated content creation.
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Add or review a paper entry in TRL's paper index. Use when a PR implements a method, algorithm, or training approach from a research paper, or when reviewing such a PR.
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Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.
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。
Multi-agent web research with mandatory URL verification, confidence-tagged output, and four depth modes (quick to deep investigation). USE WHEN research, do research, quick research, extensive research, deep investigation, find information, investigate, extract alpha, analyze content, retrieve content, AI trends, enhance content, extract knowledge, web scraping, YouTube extraction, map landscape, competitive analysis, find it, find this, find this product, identify this, what is this, what's that thing, track down, locate, help me find, I can't find X online, can't find it online, source this — never substitute raw WebSearch/WebFetch for a multi-source find/identify/investigate request. NOT FOR people/company/entity deep background, academic papers (use ArXiv), JSON entity extraction, or content-adaptive wisdom extraction (use ExtractWisdom).
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。
Interactive voice-narrated tutorial on whatever the current conversation is about — teaches the topic beat by beat with ASCII diagrams in the terminal while speaking the actual lesson content aloud, asking one question per beat and quizzing until the ideas land. USE WHEN teach me, /teach-me, tutorial, teach me this, walk me through this interactively, explain this like a lesson, learn mode, quiz me, help me actually understand what we just did. NOT FOR plain written explanations with no dialogue (just answer directly), authored/published content, or discovery interviews that shape a half-formed idea (use grill-me).
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 wants Codex to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, wants Codex to review past sessions, learn preferences, consolidate memory/skills, run dry-run/run/adopt/status for SkillOpt-Sleep, or schedule background self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay through a selected backend -> consolidate validated memory + skills behind a held-out gate.
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 wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule background self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay through a selected backend -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate.
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 wants the dsh agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, skill/memory consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule background self-optimization. Drives the skillopt_sleep engine through the skillopt_* tools: harvest past sessions -> mine recurring tasks -> replay via a selected backend -> consolidate validated skills behind a held-out gate.
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。
Reference-only OpenClaw adaptation of SkillOpt-Sleep. Use it to study or port the contributed DeepSeek wrapper, not as a ready-to-run installation.
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。