データ分析、機械学習、LLMアプリ、プロンプト設計（9,032 件）
Design focused empty states for zero-data, no-results, permission, and error situations with clear value framing, strong CTAs, and less dead chrome. Use when the user mentions blank pages, zero-data screens, no results, permission states, or dead controls—not broader onboarding strategy.
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。
Design or improve data visualizations, charts, and data presentation interfaces. Use when the user asks to add charts, build dashboards, visualize data, choose chart types, make data accessible, or present metrics and analytics.
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。
PostgreSQL database agent: deploy locally (Docker/K8s), manage schemas, run queries, analyze performance, configure permissions, and seed 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。
D2 diagramming language reference for architecture diagrams, sequence diagrams, grid layouts, SQL tables, and class diagrams. Produces .d2 files rendered via CLI or playground.
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。
Build MySQL queries, perform CRUD operations, and manage transactions using @axiosleo/orm-mysql. Use when writing database queries, building where conditions, inserting/updating/deleting rows, managing transactions, or working with the ORM query builder in this project.
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 X/Twitter from the CLI (read, post, DM, lists, communities, Grok, scheduling, analytics). Use when an agent needs to read or act on X/Twitter. Every command prints one JSON envelope.
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。
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
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 near the end of EZ_math_model intake when external/user-corpus contains user-provided papers, notes, PDFs, datasets, or examples that should become a local AGENTS.md reference index.
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。
公开数据集发现入口。覆盖 Kaggle / UCI / HuggingFace / 天池。题目要求"自行 查找数据"或"补充外部数据"时启用；附件已含数据时**不调用**。
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。
Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.
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 intuitive exploration for approaching unknown codebases, problems, or systems without preconceptions. Adapts the Coordinate Remote Viewing protocol to AI investigation: cooldown (clear assumptions), staged data gathering (raw signals → dimensional → analytical), AOL management (separating observations from premature labels), and structured review. Use when investigating an unfamiliar codebase with unknown architecture, debugging a problem where premature hypotheses could mislead, exploring a domain with limited context, or when previous attempts have been led astray by assumptions and "beginner's mind" would be more productive.
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。
Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.
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。
Plan a multi-stop tour route with waypoint optimization, drive/walk time estimation, and POI discovery along the route using OSM data. Covers geocoding, nearest-neighbor and TSP-based ordering, time/distance matrix calculation, and itinerary generation with points of interest. Use when planning a road trip or walking tour with multiple destinations, optimizing visit order to minimize travel time, discovering sites along a route, or comparing driving versus walking versus public transport options.
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 the viz pipeline to render icons from existing glyphs. Entry point for the viz subproject covering palette generation, data building, manifest creation, and icon rendering for skills, agents, and teams. Always use build.sh as the pipeline entry point — never call Rscript directly.
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。
Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.
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。
Design monochrome ornamental patterns grounded in Alexander Speltz's classical ornament taxonomy. Covers historical period selection, motif structural analysis, prompt construction for line art and silhouette rendering, and AI-assisted image generation via Z-Image. Use when creating decorative borders, medallions, or friezes in a single color, exploring historical ornament styles through generative AI, producing line art or pen-and-ink renderings of classical motifs, or generating reference imagery for design or educational materials.
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。
Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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。
Conduct structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral 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。
Organize, track, and value a trading card game collection. Covers inventory methods, storage best practices, grade-based valuation, want-list management, and collection analytics for Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when starting a new collection and setting up inventory tracking, cataloging an existing collection that has grown beyond casual knowledge, valuing a collection for insurance or sale, or deciding which cards to submit for professional grading based on value potential.
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 Quarto-based tour report with embedded maps, daily itineraries, logistics tables, and accommodation/transport details. Produces a self-contained HTML or PDF document suitable for offline use during travel. Use when compiling a planned tour into a shareable document, creating an offline-accessible travel guide, documenting a completed trip with photos and statistics, or producing a professional tour proposal for a group or client.
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。
Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
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。
Systematically interpret Raman spectra to identify molecular vibrations, assess polarizability-driven selection rules, compare with complementary IR data, and evaluate depolarization ratios for symmetry assignment. Covers Raman-active mode identification, fluorescence interference mitigation, and reference spectrum matching.
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。
Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
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。
Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.
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。