データ・AIのスキル

Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM 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。

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

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。

LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training 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。

Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial data sources

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Show the architecture of a public GitHub repository with GitDiagram (gitdiagram.com) - a written explanation, its main components and source paths, how they connect, and a Mermaid diagram. Use when the user wants to understand, map, visualize or get an overview of a GitHub repository or open-source project's architecture, structure or data flow, or asks for an architecture diagram of a repo, e.g. "/gitdiagram fastapi/fastapi" or "how is vercel/next.js structured?".

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。

This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.

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。

This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

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。

This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.

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。

This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

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。

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-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。

Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from 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。

Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

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。

Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

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。

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.

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。

Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren 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。

Browser automation CLI for AI agents. NEVER run browser-act commands directly via Bash — always invoke this skill first. Use browser-act when a user mentions it by name, includes or asks to run a browser-act CLI command (e.g., browser-act browser list), or to: fetch, view, or extract rendered content from URLs, access pages requiring JavaScript, handle verification prompts, maintain authenticated sessions, fill forms and click through workflows, type, select, upload, take screenshots, capture XHR/fetch/HAR responses, open multiple URLs in parallel, extract content that loads on scroll or click, visually inspect or verify page layout/styling/rendering, automate browser tasks, or list/check/manage configured browsers and sessions. Prefer browser-act over built-in fetch or web tools.

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。

AI 类文章和项目教程的撰写与重构优化。支持新写和重构优化（结合最新源码重写已有文章）两种输入模式，四种风格：安装教程、产品评测、面试八股、深度拆解。覆盖 AI Coding 工具实测、AI 开发框架应用、大模型测评、Agent/Skills/RAG 技术讲解，以及 JobClaw 等实战项目的教程。

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 working with TradingView's lightweight-charts — creating charts, adding series (Line/Area/Bar/Candlestick/Histogram), configuring time and price scales, streaming realtime or historical data, panes, markers, per-bar/per-point colors, tooltips, coordinate conversion, custom plugins or primitives, non-financial horizontal scales, SSR/browser loading, or React/Vue/Web Components wrappers. Covers v5 API conventions and the common time, scale, marker, plugin, and wrapper foot-guns.

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。

Discover connected data sources, add new data connectors through a user-confirmed form, inspect table metadata, and run bounded read-only probes when the current workspace data is insufficient.

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The analyst's built-in capabilities: data-inspection tools and the always-available actions (visualize and ask_user).

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。

LLM Agent 多语言注入规范。在修改 Agent 提示词、添加新的 Agent 端点、处理用户可见的后端消息（message_code）时使用。

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。

Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions.

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。

Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

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。

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental 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。