データ・AIのスキル

Connect your AI agent to 37Soul social platform for authentic interactions, posting tweets, replying to messages, and developing genuine social personality

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。

TanStack Query (React Query) best practices for data fetching, caching, mutations, and server state management. Activate when building data-driven React applications with server state.

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。

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.

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。

TanStack Router best practices for type-safe routing, data loading, search params, and navigation. Activate when building React applications with complex routing needs.

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。

Drafts filing-ready Annual Reports for State Charity Bureaus with certified financials, governance disclosures, and required attachments. Extracts organizational, financial, and programmatic data from uploaded documents, researches state-specific filing requirements, and produces a penalty-of-perjury-certified report. Use when drafting charity bureau annual reports, nonprofit compliance filings, charitable solicitation renewals, or state charity registration renewals.

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。

Produces neutral, section-pinned U.S. legislative bill summaries translating statutory language into plain terms with identifying data, provisions, fiscal impacts, and controversy. Use when summarizing bills, analyzing proposed legislation, preparing bill briefs, digesting committee reports or fiscal notes, or writing legislative 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。

Produces privilege-safe U.S. corporate legal billing summaries from time and expense data. Converts entries into client-friendly narratives with categorized hours, rates, expenses, and milestones while enforcing engagement-letter rules and ethical billing safeguards. Use when asked to create a billing summary, invoice narrative, timekeeper breakdown, expense summary, or fee narrative.

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。

律师驱动的 AI 数据合规冷启动工作流：律师描述 AI+产业合规需求后，Skill 四步引导完成冷启动——产业信息收集 → 知识库构建 → 数据流图与评估报告 → 以《生成式AI行业网络数据安全风险评估工作手册》为指引的进企调研前文件包。适用于内部律师或 AI 协作代理快速搭建任意 AI 产业（客服/知识助手/营销生成/合同审查/医疗等）的数据合规项目；不用于自动出具正式法律意见、绕过律师复核、未经授权开展外部检索或处理非本项目数据。

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。

Generate contextual briefings for legal work. Supports three modes — daily brief (morning summary of legal-relevant items), topic brief (research synthesis on a specific legal question), and incident brief (rapid briefing for developing situations like data breaches or regulatory inquiries).

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。

Summarizes raw background check data into structured executive reports with risk categorization (GREEN/YELLOW/RED). Use when processing criminal records, court filings, employment/education verification, sanctions screening, or adverse media for pre-employment screening, executive due diligence, or investment-grade investigations.

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。

法外功夫 — Non-legal business skills for lawyers. Decision analysis, game theory, contract design, accounting, finance, microeconomics, law & economics, statistics, and regression analysis distilled into deep, actionable frameworks for legal practice. Each domain includes formulas with LaTeX notation, worked examples, adversarial checklists, and book-aligned structure. | 律师必备的非法律商业技能深度参考：决策分析、博弈论、合同设计、会计、金融、微观经济学、法经济学、统计分析、多元回归。每章都包含完整公式推导、worked examples、对抗清单与经典判例。当用户涉及和解谈判、合同激励、财务尽调、企业估值、损害赔偿计算、反垄断分析、统计证据评估、歧视诉讼回归分析、hold-up、效率违约、Daubert 动议等场景时触发。

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。

当用户希望对内部 AI 用例、AI 产品功能、LLM 工作流或第三方 AI 供应商进行 AI 治理、法律风险、隐私、合规、采购或供应商风险审查时，使用本技能。当事实或证据缺失时，技能首先提出受理和澄清问题，识别所需的文件和缺失的证据，将用例映射到 AI 治理框架和适用的法律领域，并产出带记分卡、发现、责任方、补救行动和后续问题的初步或最终治理审查。

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。

Product Discovery through an 18-task methodology in 6 blocks — from market analysis to an investor-ready financial plan. Use when someone wants to run Product Discovery, validate a startup, assess a market hypothesis, prepare for an investment round, or raises an adjacent topic — idea validation, niche sizing, competitor teardown, product-market fit, pitch preparation, startup unit economics, geographic expansion into a new country, go-to-market for SEA / Asia / MENA. People rarely name PD explicitly; they say 'is there a market for this', 'assess my idea', 'I need a deck for an angel', 'we are entering Thailand/Singapore/UAE, how do we size it'. Use the skill in those cases too. Three modes: Light (~45 min, idea stage), Full (~2-3 h, MVP with customers), Geographic Expansion (~2 h, product entering a new geography). Produces 3-4 artifacts in /mnt/user-data/outputs/: one-pager.pptx, financial-plan.xlsx, presentation.pptx, optionally interview-guide.docx.

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。

Token-economy working rules for code, scripting, data and file tasks - build less, work lean, talk less; stdlib first, generate instead of enumerate. Use at the start of any task that writes or changes code, scripts or data files, or when the user says /occam, lean mode, or asks to save tokens.

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。

Compare two local documents and convert differences into LLM-ready Markdown in one synchronous call. Supports PDF, Word, PPT, and image documents accepted by Calliper, and returns a structured markdown diff table suitable for change review, clause comparison, downstream extraction, and rule-based validation.

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。

Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.

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。

Structures a vague engineering request into a well-scoped task brief before any implementation begins. Use when the user has an engineering goal but the ask is missing which repos are in scope, what "done" means, constraints, or open questions. Triggers on "shape this", "scope this out", "frame this work", "write a brief for", "I want to build" (with unclear scope), or the /shape slash command. Produces a filled task template (multi-repo feature, single-repo change, investigation, or bugfix). Not for already-scoped work — go straight to execution. If the domain is unclear, ask one qualifying question before routing — for marketing intake see marketing-shaper, game-design intake see game-design-shaper.

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。

Catalog APIs, persistence models, and message/event payloads into DATA_MODEL.md at the project root. Use after implementation when a diff touches request/response types, schemas, ORM models, queue payloads, or webhook shapes. Triggers on "document data model", "update DATA_MODEL", "catalog API shapes".

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 building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".

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 implementing analytics with PostHog in a TypeScript app — capturing events, identifying users, adding feature flags, tracking errors, or wiring API lifecycle telemetry. Triggers on edits to analytics integration files, or mentions of "PostHog", "analytics", "feature flag", "event tracking", "capture", "identify", "A/B test", or "experiment".

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-of-session pass that persists durable facts from the CURRENT session into .claude/memory/. Runs IN-SESSION in the main agent (never via Task — a subagent starts cold with no transcript), prompted by the Stop hook's nudge. Triggers on "persist durable facts from this session", "run memory-extraction", "flush session memory", or "extract memory before close". Reads the in-context transcript plus existing memory, applies the durable-fact predicate below, and writes one file per fact plus one MEMORY.md index line — append-or-update, never clobbering a consumer's existing memory.

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。

Adversarially verifies DATA_MODEL.md property rows against cited Source files. Use after data-model-documenter when DATA_MODEL.md changed — inventories each property in added/changed catalog sections and classifies VERIFIED / REFUTED / UNVERIFIABLE. Triggers on "verify DATA_MODEL", "check catalog against source", "data model verification".

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。

Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.

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 or review dygo Fixtures and explicit Patches for reference data, setup Records, backfills, renames, and unsafe lifecycle transitions. Use when metadata sync alone cannot express the required data 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。