「engineer」の検索結果

Build or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions. Use when an agent must profile and safely prepare uploaded data, turn a real dataset or research question into a reproducible study, investigate drivers without overstating causality, validate a model, compare feasible actions under dependent uncertainty and tail risk, trace every parameter to evidence and approval, or produce an answer-first analytical report across health, business, finance, policy, engineering, operations, behavioral science, AI, or planning.

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 structured onboarding experience that gets new team members productive in 30, 60, and 90 days. Use when a new hire is joining, when contractors or agency partners need to ramp up, when an existing team is restructuring and members are switching focus, or when current onboarding feels chaotic and slow. Also triggers when one person owns all the tribal knowledge and you need to capture it, when you keep losing people in their first 90 days, or when a new project has many fresh members joining at once. Useful for engineering, design, product, marketing, and operations roles.

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。

Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time.

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 an operating runbook from the delivered system and verified procedures. Use when the customer team or a successor needs to operate without the original 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。

Transfer operation of a customer deployment with ownership, evidence and a tested support path. Use for handoff or an engineer rotation, not merely code delivery.

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。

Protein engineering and directed evolution analysis using Adaptyv Bio platform

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。

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics. Per-person breakdowns with praise and growth areas. Modes: default 7d, 24h, 14d, 30d, compare, global cross-project. Shipping streaks and tweetable summary.

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。

Builds real-time communication systems with WebSockets or Socket.IO including bidirectional messaging, horizontal scaling with Redis, presence tracking, and room management.

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。

Writes Spark jobs, debugs performance issues, configures cluster settings for distributed data processing, DataFrame transformations, and structured streaming 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。

Implements infrastructure as code with Terraform across AWS, Azure, or GCP including module development, state management, provider configuration, and multi-environment workflows.

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。

Generates Spring Boot 3.x configurations, creates REST controllers, implements Spring Security 6, sets up Spring Data JPA, and configures reactive WebFlux endpoints.

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。

Writes idiomatic Rust code with memory safety, zero-cost abstractions, ownership patterns, lifetime management, trait hierarchies, and async applications with tokio.

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。

Writes, refactors, and evaluates prompts for LLMs generating optimized templates, structured output schemas, evaluation rubrics, and test suites.

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。

Reverse-engineers specifications from existing codebases, maps code dependencies, generates API documentation from source, and identifies undocumented business logic.

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。

Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations.

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。

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems with runbooks, experiment manifests, and rollback procedures.

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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates for deployment automation and GitOps.

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, build, and operate Kubernetes operators. Use when extending Kubernetes with a custom controller, choosing a framework, designing CRDs, implementing reconciliation loops, or auditing an operator for anti-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。

Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.

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。

This skill should be used when the user asks to "estimate LLM costs", "count tokens in prompts", "optimize prompt token usage", "compare model pricing", or "reduce LLM API costs".

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。

Feature flag strategy, lifecycle, and operations. Use when designing a flag taxonomy, planning a gradual rollout, building kill switches, auditing flag debt, defining governance, running progressive delivery, or rolling back via flags.

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 data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.

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。

Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.

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。

MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.

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。