876 件 ・ 関連度順
概要と使いどころ
Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.
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 statistically rigorous experiments to test data science hypotheses. Use when the user says "design an A/B test", "run an experiment", "test this hypothesis", "is this difference significant", "how many samples do I need", "randomized controlled trial", "causal inference", "uplift test", "holdout group", "significance test", "power calculation", "avoid p-hacking", or needs to determine whether an observed effect is real before making a product or model decision.
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。
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).
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。
Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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。
Configure or troubleshoot Health Cloud Care Programs: CareProgram hierarchy, patient enrollment (CareProgramEnrollee), consent prerequisites, program products (CareProgramEnrolleeProduct), provider associations (CareProgramProvider). Trigger keywords: care program, program enrollment, enrollee, program product, consent for enrollment, patient program outcome. NOT for care plan templates, problems and goals - use admin/care-plan-configuration. NOT for patient records, person accounts and care team roles - use admin/health-cloud-patient-setup.
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。
Materials engineering expert covering properties, processing, testing, and applications of 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。
Expert guidance across solar physics, planetary science, stellar evolution, cosmology, and observational techniques with 2025 mission 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。
Ocean science expert covering physical, chemical, biological, and geological oceanography
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 in combinatorics, graph theory, discrete probability, and algorithms with applications to computer science
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 in vector spaces, matrices, linear transformations, eigenvalues, and applications to data science and machine 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。
Run a political science pre-submission audit with journal-aware personas, stage-aware standards, and evidence-grounded issue reporting.
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。
Unified verification engine for Python data science projects. Covers environment checks, type checking, linting, tests, security scans, code review with DS anti-patterns, and notebook checks. Commands (/verify, /quality-gate) invoke different subsets of this skill.
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。
Scientific research in serendipity mode. Infinite loops until discovery, rigorous tracking, adversarial 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。
Scientific research engine with agentic tree search. Infinite loops until discovery, rigorous tracking, adversarial review, serendipity preserved.
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 preparing or writing any committable deliverable (phase closeout, status report, skill file, wave spec, README section, summary document, CHANGELOG entry, or any markdown file declaring completion or pass/fail status). Applies before the write happens, not after. Without this discipline, agents ship 60% deliverables and declare closure prematurely.
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。
Refine academic writing for computer science research papers targeting top-tier venues (NeurIPS, ICLR, ICML, AAAI, IJCAI, ACL, EMNLP, NAACL, CVPR, WWW, KDD, SIGIR, CIKM, and similar). Use this skill whenever a user asks to improve, polish, refine, edit, or proofread academic or research writing — including paper drafts, abstracts, introductions, related work sections, methodology descriptions, experiment write-ups, or conclusion sections. Also trigger when users paste LaTeX content and ask for writing help, mention "camera-ready", "rebuttal", "paper revision", or reference any academic venue or conference. This skill handles both full paper refinement and section-by-section editing.
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。
Core statistical methodology and pedagogical approach for all Inquiro analyses. Always loaded to maintain teaching quality and reproducibility standards.
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。
Summary statistics, data exploration, and descriptive analysis. Use for: summary stats, describe data, data exploration, distributions, means, medians, correlations, cross-tabs.
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 data from randomized controlled trials. Use when user mentions: treatment effects, ITT analysis, LATE, TOT, compliance analysis, attrition, balance checks, heterogeneous effects, RCT results, experimental analysis.
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 randomized controlled trials for causal inference. Use when user mentions: randomized evaluation, RCT, field experiment, randomized experiment, treatment assignment, causal impact, experimental design, control group, intervention evaluation.
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。
Publication-quality statistical visualization with mandatory best practices. Use when creating plots, charts, figures, or any data visualization. Enforces accessibility, uncertainty display, proper labeling, and statistical accuracy.
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。
Implement randomization methods for RCTs. Use when user mentions: random assignment, treatment allocation, stratification, blocking, permutation randomization, cluster randomization, randomization balance, misfit handling, re-randomization.
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。
Causal inference with treatment/control groups over time. Use when user mentions: treatment effects, policy evaluation, pre/post comparison, parallel trends, DID, DiD, difference in differences, natural experiment, quasi-experimental.
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。
Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
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。