“modular ai”的搜索结果

Apply a specific set of Rails conventions to design, implementation, and code review. It keeps business logic in models, represents state changes as records, limits dependencies, and uses Minitest with fixtures, or predefined test data.

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• Designing Rails models and database changes
• Representing state changes with separate records
• Reviewing code for consistent Rails conventions

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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Design and review how a system is divided into modules, cohesive parts with clear responsibilities. Evaluate data ownership, communication rules, dependencies, and failure handling, and decide when to split or merge parts without assuming a particular technology.

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• Define responsibilities and boundaries for a new module
• Review dependencies between system modules
• Clarify data ownership and communication rules

Expert guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs microservices, implementing aggregates/entities/value objects, or when users mention "DDD", "domain-driven design", "bounded context", "aggregate", "domain model", "ubiquitous language", "event storming", "context mapping", "domain events", "anemic domain model", strategic design, tactical patterns, or domain modeling. Helps make architectural decisions, identify subdomains, design aggregates, and avoid common DDD pitfalls.

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Design a modular monolith, a single application with clear business boundaries. Plan module structure, external service integration, failure handling, and future separation, then produce an HTML architecture document with SVG diagrams.

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• Design business boundaries for a new backend
• Plan integration that limits dependence on an external vendor
• Define retries and failure handling for external calls

Analyze a monolithic codebase before splitting it into modules or services. The skill inventories components, examines duplication, hierarchy, and dependencies, then proposes groupings aligned with business domains, with optional checks against business terminology and boundaries.

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• Assessing component sizes before splitting a monolith
• Finding duplicated business logic across modules
• Reviewing module hierarchy and dependencies

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

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• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

Design and implement a NestJS backend organized into business modules within one deployable application. The skill defines module boundaries, separates data ownership, builds layered code and event communication, and validates isolation through checks and tests.

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• Designing a new modular NestJS backend
• Implementing business modules with layered responsibilities
• Connecting modules through events

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

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• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Building modular, debuggable AI behaviors using behavior trees for game NPCs and agentsUse when "behavior tree, bt, npc ai, ai behavior, game ai, decision tree, blackboard, ai, behavior-trees, npc, game-ai, decision-making, agents" mentioned.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Despliega soluciones de IA para insurance claims automation con arquitectura modular, metricas auditables y decisiones alineadas al contexto del dominio.

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Build a modular daily-planner one-sheet for an e-ink tablet and deliver it via Folio. Use when the user asks for a daily plan, daily planner, "daily one sheet", morning sheet, day plan, or to send a planner to their reMarkable, Supernote, Kindle Scribe or other e-ink device. Interviews the user, pulls from connected tools (calendar, tasks, GitHub, weather), renders an e-ink-optimized PDF sized for the chosen device, and sends it with Folio.

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Build, run, and troubleshoot data processing pipelines with Nextflow and nf-core. The skill guides modular workflow development, input preparation, testing, cached restarts, and configuration for local computers, computing clusters, or cloud environments.

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• Run an nf-core workflow with a samplesheet
• Build and test reusable DSL2 modules
• Investigate failed runs and restart with cached results

Build Splunk pipelines that import threat intelligence and match security events against indicators such as malicious IP addresses, domains, and file hashes. Add source, confidence, and severity details so analysts have context when investigating matches.

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• Importing external threat feeds into Splunk
• Matching network and endpoint logs against threat indicators
• Adding threat and asset context to alerts

Expert-level number theory knowledge. Use when working with divisibility, prime numbers, modular arithmetic, congruences, Diophantine equations, cryptography, quadratic residues, or analytic number theory. Also use when the user mentions 'prime', 'divisibility', 'modular arithmetic', 'congruence', 'GCD', 'Euler totient', 'Fermat little theorem', 'Chinese remainder theorem', 'quadratic residue', 'Diophantine equation', 'RSA', or 'prime factorization'.

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Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

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• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Restructure claude-flow v3 into focused modules using Domain-Driven Design, which groups code by responsibility. Define module boundaries, event communication, and plugin interfaces to support a modular architecture with independently testable business logic.

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• Split an oversized orchestrator into focused modules
• Define boundaries between tasks, sessions, and monitoring
• Design plugin extensions and domain events

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

中文简介正在准备中。当前显示英语简介。

• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Restructure claude-flow v3 into focused modules using Domain-Driven Design, which groups code by responsibility. Define module boundaries, event communication, and plugin interfaces to support a modular architecture with independently testable business logic.

中文简介正在准备中。当前显示英语简介。

• Split an oversized orchestrator into focused modules
• Define boundaries between tasks, sessions, and monitoring
• Design plugin extensions and domain events

Use for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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