데이터·AI 스킬

予測モデリングのワークフローを実装・評価する。引数: <dataset_path> <target> <task>

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Use this when writing, reviewing, or modifying SQL queries — including SELECT, CTEs, joins, aggregations, window functions, and validating query correctness or performance.

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Use this when performing DataFrame operations — including loading, filtering, joining, aggregating, transforming, or reshaping tabular data with polars or pandas.

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Use this when reading, writing, moving, copying, modifying, deleting, or generating data files — including any operation that touches data/raw, data/external, data/interim, data/processed, or outputs directories.

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教師なし分析と EDA 前処理（欠測 / 欠損 / NaN / 補完 / imputation / 外れ値処理、クラスタリング / セグメンテーション / k-means / 階層クラスタリング / GMM / DBSCAN、次元削減 / PCA / 主成分分析 / 因子分析 / UMAP / t-SNE、異常検知 / anomaly detection / 不正検知）を実行したら必ずセットで出す図と値のルーター。fillna, dropna, fill_null, SimpleImputer, IterativeImputer, missingno, KMeans, AgglomerativeClustering, DBSCAN, GaussianMixture, silhouette_score, dendrogram, PCA, FactorAnalysis, calculate_kmo, TSNE, umap.UMAP, IsolationForest, LocalOutlierFactor, pyod がコードに現れたとき、またはユーザーが「欠損を埋めて」「クラスタリングして」「主成分で 要約して」「異常を検知して」「データをきれいにして」と言ったときに使う。安定性・k の根拠・平行分析・ベースラインに 言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を読んでから実行する。 教師あり予測は predictive-modeling-diagnostics、回帰・検定は statistical-inference-diagnostics を使う。

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データセットのEDAを実装・実行する。引数: <dataset_path> <topic>

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SQLクエリの正確性と安全性をレビューする

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Scans a codebase and generates a self-contained HTML service-design journey map — NN/g combined customer journey + service blueprint format. Use when the user wants to map user journeys, create journey maps, build service blueprints, or understand user flows from code. Prompts for codebase path if not supplied. Works with any tech stack.

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Smithy the Toolsmith builds, converts, reviews, packages, validates and migrates BMad skills, agents and modules. Use when the user wants a skill, agent, persona, command or module made, changed, reviewed or packaged; a prompt or rule from another tool turned into a skill; a skill mined from session logs; an old module migrated; or asks for Smithy. Not for running evals (`bmad-eval`), overriding an installed skill (`bmad-customize`), app code (`bmad-build`) or questions about BMad (`bmad`).

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Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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RAG & Knowledge Architect: Retrieval systems: ingestion, chunking, embeddings, hybrid search, reranking, evals.

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Author and run a durable AI chat agent with chat.agent from @trigger.dev/sdk/ai: the per-turn run loop, why you MUST spread ...chat.toStreamTextOptions() first, returning a StreamTextResult vs calling chat.pipe(), the two server actions (chat.createStartSessionAction + auth.createPublicToken), and wiring useChat to useTriggerChatTransport. Load this when building, modifying, or debugging a chat backend (the agent task or its lifecycle hooks) or its React transport, when declaring typed tools or custom data parts, or when migrating a plain AI SDK streamText route to chat.agent.

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Overview of the Neon platform for apps and agents, spanning Postgres, Auth, Data API, and the new services: Object Storage, Compute Functions, and AI Gateway. Use whenever "Neon" is mentioned for an overview of how to work with Neon and how to get started. Otherwise, the individual capabilities are the triggers: "object storage" or "S3-compatible storage", "serverless functions", "background jobs", or "run code near my database", "AI gateway", "LLM proxy", "model routing", or "call an LLM" → AI Gateway; "database", "Postgres", or "authentication" → Postgres and Auth.

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Generate images via Nano Banana with 129 curated prompts. Mandatory validation interview refines style/mood/colors (use --skip to bypass). 3 modes: search, creative, wild. Styles: Ukiyo-e, Bento grid, cyberpunk, cinematic, vintage patent.

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多模型镜头提示词适配：把一份分镜表改写成适合不同视频生成模型的提示词（中/英、首帧/首尾帧/纯文生视频、运镜词表、负面约束），模型中立、不编造价格或参数。Use when the user wants the same shot list adapted for Seedance, 可灵 Kling, Veo, Runway, Hailuo/海螺, Wan/通义万相, Sora, Pika or 'any video model', asks 怎么写视频提示词, camera movement terms, or a model returns deformed products.

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Generate Muted Zine Poster v0.1 poetic paper-poster prompts and the matching generated image. Use when the user gives a theme, sentence, object, mood, article idea, photo, or content brief and wants a quiet Japanese/Korean zine-like editorial poster with large negative space, aged paper texture, experimental typography, and a restrained muted palette without high-chroma color blocks. This is the low-saturation sibling of gc-minimal-zine-poster-v0-1.

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Internal Harness instruction source for data-prep. Route through visible Harness aliases instead of invoking directly.

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Internal Harness instruction source for auto-paper-data. Route through visible Harness aliases instead of invoking directly.

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Acquire and verify finite environment, data, weight, and baseline inputs.

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Use when user requests diagrams, flowcharts, sequence diagrams, class diagrams, component diagrams, ER diagrams, architecture charts, or visualizations — including generating a diagram from existing source code, or rendering/extracting PlantUML embedded in a Markdown file to images (e.g. preparing docs for Confluence/Notion). Also use proactively when explaining systems with 3+ components, APIs, data flows, or class hierarchies. Generates .puml files and exports to PNG/SVG via Kroki API (no local install required).

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Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .tldr JSON files and exports to PNG/SVG locally using @kitschpatrol/tldraw-cli.

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Use whenever the user mentions Zenodo, depositing or publishing research artifacts (datasets, software, papers, posters) to Zenodo, minting a DOI for a dataset/code release, uploading files to a Zenodo record, creating a new version of a Zenodo deposit, or searching Zenodo records. Covers the full Zenodo REST API workflow — create deposition, upload files via the bucket API, set metadata, publish, version, and search — for both production (zenodo.org) and sandbox (sandbox.zenodo.org).

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Fetch and export public X (Twitter) data with a self-contained Python CLI (twikit; no API keys, uses the user's own login cookies): profiles, a user's tweets/replies/likes, threads, search, followers, timelines, JSON/JSONL/CSV/SQLite output, resumable pagination. Use to scrape, archive, monitor, or analyze tweets or accounts.

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Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.

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