「fea」の検索結果

Test-first development route for TDD, writing failing tests first, RED -> GREEN -> REFACTOR, and behavior-changing feature/bug/refactor work. Do not use for already-failing test root-cause debugging, test-report packaging, or final completion evidence.

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 technical implementation plans from feature specifications. Use after creating a spec to define architecture, tech stack, and implementation phases. Creates plan.md with detailed technical design.

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。

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

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。

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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。

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

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。

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production 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。

Prowler documentation style guide and writing standards. Trigger: When writing documentation for Prowler features, tutorials, or guides.

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 structured test reports with pass/fail rollups, coverage summaries, and test artifacts. Use when the user is asking for test-result packaging or delivery, not for root-cause debugging or feature implementation.

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。

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

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。

Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

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。

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

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。

Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.

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 codebase patterns, conventions, and unwritten rules before making changes. Use when implementing features, fixing bugs, or refactoring code.

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。

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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。

Verify completion and success claims with fresh evidence. Use before claiming a task is complete, a fix works, tests pass, or a feature is ready for GO.

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 complete specs (requirements, design, tasks) for all features in roadmap.md using parallel subagent dispatch by dependency wave.

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 complete specs for roadmap features by dependency wave using Devin subagents when available, with permission-aware sequential and inline fallbacks.

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。

Validate feature-level integration after all tasks are implemented. Checks cross-task consistency, full test suite, and overall spec coverage.

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 complete specs (requirements, design, tasks) for all features in roadmap.md sequentially by dependency wave in Cascade.

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 complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

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。

Modern, clean UI/UX guidance + review skill. Use when you need actionable UX/UI recommendations, design principles, or a design review checklist for new features or existing systems (web/app). Focus on CRAP (Contrast/Repetition/Alignment/Proximity) plus task-first UX, information architecture, feedback & system status, consistency, affordances, error prevention/recovery, and cognitive load. Enforce a modern minimal style (clean, spacious, typography-led), reduce unnecessary copy, forbid emoji as icons, and recommend intuitive refined icons from a consistent icon set.

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。

Automate PostHog tasks via Rube MCP (Composio): events, feature flags, projects, user profiles, annotations. Always search tools first for current schemas.

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, update, and apply iOS design specifications using Apple Human Interface Guidelines (HIG) source data. Use when a task asks for iOS UI/UX rules, Apple design standards, component behavior, accessibility constraints, interaction patterns, or feature-level design-spec writing grounded in official HIG pages.

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。

Triggers the WORK-PIPELINE when a user request starts with a [] tag (e.g., [new-feature], [bugfix], [WORK start]). Use this skill whenever you detect a [] tag at the beginning of a user message.

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。