「ema」の検索結果

Builds comprehensive design systems and design bibles with production-ready CSS. Expert in design tokens, component libraries, CSS architecture. Use for design system creation, token architecture, component documentation, style guide generation. Activate on "design system", "design tokens", "CSS architecture", "component library", "style guide", "design bible". NOT for typography deep-dives (use typography-expert), color theory mathematics (use color-theory-palette-harmony-expert), brand identity strategy (use web-design-expert), or actual UI implementation (use web-design-expert or native-app-designer).

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。

Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).

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 data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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。

PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).

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。

Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies. Activate on: React Query, SWR, optimistic updates, cache invalidation, prefetching, stale-while-revalidate, infinite queries. NOT for: REST API design (use api-architect), database queries (use database skills), GraphQL schema (use graphql-expert).

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。

Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).

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。

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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。

Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.

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 implement systematic checklists that reduce errors by 30-50% in complex, high-stakes domains (medicine, aviation, construction, finance). NOT for simple tasks or when comprehensive instruction is needed.

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。

Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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 background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.

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。

Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).

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。

Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.

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 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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。

Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.

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。

Store and semantically search agent memory with ONNX vector embeddings on a ruOS desktop, using the Ruflo CLI. Use to persist patterns/decisions across sessions and recall them by meaning rather than exact key.

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。

Persistent, adaptive vector memory for agents on a ruOS desktop — a Rust-native vector database with local semantic embeddings, HNSW retrieval, and graph relationships. Use to remember and recall across sessions with no database server or API key.

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。

Search for code patterns in decompiled output using Weggli semantic matching. Use when finding vulnerable code constructs like unchecked memcpy, buffer operations, or specific function call patterns in pseudocode.

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。

Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.

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。

Security launch audit for a public web app BEFORE you ship or hand it to a client — "vibe coders are getting sued", systematized. An executable pipeline: classify (static / full-stack / AI) → run sections (secrets+supply-chain, DB, auth, infra+headers, legal, LLM/injection) orchestrating deterministic tools + manual checks → GO/NO-GO report. Adapted from Prajwal Tomar's playbook, extended with prompt-injection / supply-chain / secret-history / dependency-CVE layers. NOT the same as pre-push (that = clean push; this = is the product itself safe to launch). Triggers — "security checklist for the site", "audit before launch", "is the site safe", "check before handing to 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。

Phased workflow for delivering a Drupal feature or non-trivial change: plan, implement, review, test, finalize. Use when building a new custom module, adding a route/form/plugin, changing entity structure or schema, or any task touching more than a couple of files. Do NOT use for typo fixes, CSS tweaks, or single-line changes — use the tiny-fix skill instead. Each phase has an exit condition; do not move to the next phase until it is met.

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。

Low-ceremony workflow for trivial, low-risk changes: typos, comments, CSS/styling tweaks, string changes, single-line fixes with no logic impact. Use to avoid the overhead of the full feature workflow when the change cannot break anything. If during the fix the change turns out to touch logic, security, config, or schema, stop and escalate to the feature-workflow 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。

Apply GEODE's measured architecture, module and symbol naming, Python type/class, persisted schema, test, site, and versioning conventions. Use when adding or moving modules, choosing dataclass/Pydantic/Protocol/ABC forms, naming classes or files, designing stored data, reviewing convention compliance, or deciding compatibility and release version treatment.

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。

GEODE evidence-first development workflow. Use for feature work, provider/model changes, GUI/computer-use work, PDF/document ingestion, observability/schema/event/state changes, verification planning, GitFlow merge prep, and requests to "follow the workflow" or "scaffold the process".

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。