「fea」の検索結果

Implement SAFe methodology in Jira. Use when creating Epics, Features, Stories with proper hierarchy, acceptance criteria, and parent-child linking.

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 coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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。

Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.

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 structured threat-modeling session for a new feature, system, or architecture — STRIDE, attack trees, data flow diagrams, abuse cases. Use when the user mentions 'threat model,' 'threat modeling,' 'STRIDE,' 'attack tree,' 'abuse case,' 'data flow diagram,' 'DFD,' 'security architecture review,' 'security review,' 'design review,' 'pre-implementation security,' 'shift left,' 'what could go wrong,' or needs strategic security thinking before code is written.

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。

Audit applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when the user mentions 'prompt injection,' 'LLM security,' 'AI security,' 'jailbreak,' 'indirect prompt injection,' 'prompt leaking,' 'AI red team,' 'LLM vulnerabilities,' 'AI input validation,' 'system prompt extraction,' 'agent security,' 'MCP security,' 'AI permissions,' 'AI privilege escalation,' or needs to secure any application with AI features, AI agents, or LLM integrations.

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。

Creates isolated git worktrees with smart directory selection and safety checks. Use when starting isolated feature work.

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 completing tasks, implementing major features, or before merging to verify work meets requirements

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。

Crafts story pitches for different outlets. Use when pitching editors, writing query letters, or shaping feature or op-ed angles.

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。

Explores intent and design before implementation. MUST use before creating or modifying any feature, component, or behavior.

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。

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

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。

ML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold 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。

Generate AI avatar and digital persona video prompts for Seedance 2.0 on Higgsfield. Use for virtual spokesperson content, digital twin videos, AI presenter clips, avatar-based marketing, virtual influencer content, or any video featuring a digital/AI-generated character as the main subject. Triggers on avatar, digital persona, virtual presenter, AI character, digital twin, virtual influencer, synthetic media, animated spokesperson.

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 customer testimonial and social proof video prompts for Seedance 2.0 on Higgsfield. Use for customer stories, case study videos, review showcases, social proof content, success story videos, or any content featuring customer results and experiences. Triggers on testimonial, review, case study, social proof, customer story, success story, results, feedback, 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。

Create a GitHub issue with a drafted title and body. Use when the user asks to "create an issue", "file an issue", "open an issue", "submit an issue", "report a bug", "file a bug report", "file a feature request", or "file a design proposal".

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。

Enforce existence, reuse, mirror, and symmetry principles to keep new code minimal and consistent with surrounding code. Use when writing new code in an existing codebase, adding new features, refactoring, or making any code changes.

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。

Assist with core product management activities including writing PRDs, analyzing features, synthesizing user research, planning roadmaps, and communicating product decisions. Use when you need help with PM documentation, analysis, or planning workflows that integrate with your codebase.

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。

Consult PostgreSQL's pg_dump implementation for guidance on system catalog queries and schema extraction when implementing pgschema features

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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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 toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.

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 planning documentation for web projects with context-safe phases, verification criteria, and exit conditions. Creates IMPLEMENTATION_PHASES.md plus conditional docs (DATABASE_SCHEMA, API_ENDPOINTS, UI_COMPONENTS, CRITICAL_WORKFLOWS). Use when: starting new Cloudflare Workers/React projects, adding major features to existing apps, breaking large work into manageable phases, or need verified planning before coding begins.

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。

Consult PostgreSQL's parser and grammar (gram.y) to understand SQL syntax, DDL statement structure, and parsing rules when implementing pgschema features

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 a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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。

Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.

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。

Cargo.toml dependency management patterns for HASH workspace. Use when adding dependencies, managing workspace dependencies, organizing Cargo.toml sections, setting version pinning, configuring default features, or working with public dependencies.

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。