Claude Code Skills · page 113

Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.

Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).

Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.

Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.

Managing secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.

Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

Write robust, portable shell scripts with proper error handling, argument parsing, and testing. Use when automating system tasks, building CI/CD scripts, or creating container entrypoints.

Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.

Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.

Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD integration.

Provides design token system and theming framework for consistent, customizable UI styling across all components. Covers complete token taxonomy (color, typography, spacing, shadows, borders, motion, z-index), theme switching (CSS custom properties, theme providers), RTL/i18n support (CSS logical properties), and accessibility (WCAG contrast, high contrast themes, reduced motion). This is the foundational styling layer referenced by ALL component skills. Use when theming components, implementing light/dark mode, creating brand styles, customizing visual design, ensuring design consistency, or supporting RTL languages.

Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.

Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).

Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.

Write GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.

Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.

Deep-dive codebase analysis that explains how things actually work — business rules, architecture patterns, auth flows, data models, integrations, and performance hotspots. Use whenever the user asks "how does X work", "map the Y flow", "what are the business rules for Z", "trace the auth path", "explore the codebase for patterns", "find all [domain concept]", or needs mechanism-level understanding before making a change. Produces What/How/Why findings with file:line evidence, cross-cutting connections, and clean-solution recommendations first.

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.

Create or update a project constitution with governance rules. Uses discovery-based approach to generate project-specific rules.

Systematically diagnose and resolve bugs through conversational investigation and root cause analysis

Generate and maintain documentation for code, APIs, and project components

Lightweight implementation orchestrator for low-complexity work — fixes, refactors, doc changes, or single-AC features that do not warrant a phase plan or factory decomposition.

Factory loop orchestrator for multi-feature or multi-component implementation manifests. Use for high-complexity work with parallel-eligible workstreams and holdout-scenario evaluation.

Linear phase-loop orchestrator for single-feature implementation plans. Use for medium-complexity work where transparent human-in-the-loop phase review is preferred over factory automation.

Implementation entry point. Use to execute a completed specification. Auto-detects the decomposition tier (Direct, Incremental, or Factory) from spec artifacts and dispatches to the matching execution sub-skill.

Refactor, simplify, or clean up code for improved maintainability without changing business logic

Multi-agent code review with specialized perspectives (security, performance, patterns, simplification, tests)

Decompose a multi-feature or multi-component specification into factory-consumable artifacts. Use this for high-complexity work — multiple features, three or more components, or parallel-eligible workstreams.

Decompose a single-feature specification into a linear, phase-by-phase implementation plan. Use this for medium-complexity work — single feature, one or two components — where transparent human-in-the-loop phase review is preferred over factory automation.

Scaffold, status-check, and manage specification directories. Use when creating a new spec, reading spec status, transitioning between phases, or logging decisions on a spec in .start/specs/.

Create and validate product requirements documents (PRD). Use when writing requirements, defining user stories, specifying acceptance criteria, analyzing user needs, or working on requirements.md files in .start/specs/.

Create and validate solution design documents (SDD). Use when designing architecture, defining interfaces, documenting technical decisions, analyzing system components, or working on solution.md files in .start/specs/.

Create a comprehensive specification from a brief description. Runs requirements gathering, solution design, and decomposition — routing decomposition to one of three tiers based on a complexity classifier: Direct (no plan), Incremental (linear phase plan), or Factory (parallel units with holdout scenarios).

Validate in any of six modes — spec quality, single-file review, spec-to-implementation drift, constitution rule enforcement, comparison between two artifacts, or sanity-checking your understanding. Use when checking a spec by ID, validating a file by path, detecting drift, enforcing constitution rules, comparing two sources ("$X against $Y"), or asking a freeform validation question.

Use when creating new skills, editing existing skills, auditing skill quality, converting skills to markdown conventions, or verifying skills before deployment. Triggers include skill authoring requests, skill review needs, or "the skill doesn't work" complaints.

RICE, MoSCoW, Kano, and value-effort prioritization frameworks with scoring methodologies and decision documentation. Use when prioritizing features, evaluating competing initiatives, creating roadmaps, or making build vs defer decisions.

Identify existing codebase patterns (naming conventions, architectural patterns, testing patterns) to maintain consistency. Use when generating code, reviewing changes, or understanding established practices.

Unified codebase discovery across structure navigation, tech-stack detection, and documentation extraction. Use when onboarding to a project, locating implementation paths, identifying frameworks/tooling, or validating docs against code reality.

Requirement gathering techniques, stakeholder analysis, user story patterns, and specification validation. Use when clarifying vague requirements, resolving conflicting needs, documenting specifications, or validating requirements with stakeholders.

User research and insight synthesis for interviews, usability testing, personas, journey mapping, and actionable product/design recommendations. Use when planning studies, collecting evidence, synthesizing patterns, or converting findings into prioritized decisions.

Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.

REST and GraphQL API design patterns, OpenAPI/Swagger specifications, versioning strategies, and authentication patterns. Use when designing APIs, reviewing API contracts, evaluating API technologies, or implementing API endpoints.

System architecture patterns including monolith, microservices, event-driven, and serverless, with C4 modeling, scalability strategies, and technology selection criteria. Use when designing system architectures, evaluating patterns, or planning scalability.

Unified domain and data modeling for business entities, invariants, schema design, aggregate boundaries, and evolution strategy. Use when designing business models, schema changes, bounded contexts, or consistency rules.

Context enrichment for frontend UI development using shadcn/ui and Tailwind CSS. Use when building component libraries, implementing UI designs, theming, or working with accessible React components.

Create architectural decision records (ADRs), system documentation, API documentation, and operational runbooks. Use when capturing design decisions, documenting system architecture, creating API references, or writing operational procedures.

Writing effective tests and running them successfully. Covers layer-specific mocking rules, test design principles, debugging failures, and flaky test management. Use when writing tests, reviewing test quality, or debugging test failures.

Unified platform operations guidance for CI/CD pipeline design, deployment strategies, observability, SLI/SLOs, and incident-ready rollouts. Use when building release workflows, production monitoring, or reliability controls.

Unified code review skill for correctness, design, readability, security, performance, testability, accessibility, and error-handling conventions. Use when reviewing changes, enforcing quality standards, or identifying technical debt.

Measurement approaches, profiling patterns, bottleneck identification, and optimization guidance. Use when diagnosing performance issues, establishing baselines, identifying bottlenecks, or planning for scale. Always measure before optimizing.

Vulnerability review, threat modeling, OWASP patterns, and secure coding assessment. Use when reviewing code security, designing secure systems, performing threat analysis, or validating security implementations.

Open a live Token Tracker sidebar for only the current Kimi Code session in a right-side terminal split at one-third width. Use when the user explicitly invokes /skill:tt-sidebar or asks to monitor the current session beside its terminal pane.

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Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Show the wanted-poster bounty board: this repo's TODO/FIXME/HACK debt priced as aging XP bounties

Show this repo's context-cost leaderboard: the files that pulled the most tokens into context

List this repo's recorded dead ends: approaches already tried and abandoned, with reasons and cost

Show the CLAUDE.md compliance scorecard: which rules Claude actually follows vs ignores

Show your personal model-quality trend card (failure rate, edit churn, tokens/task by model version)

Preview the provenance receipt (prompts, est. spend, tests run, agent-authored lines) for the current session ledger

Render today's standup card from what your agents actually did across repos

Autonomy contract and routing index for autonomous project controllers. Loaded every tick; carries the rules that must survive cron-prompt rewrites.

Delegate coding/automation tasks to sandboxed.sh missions via the mcp_sandboxed_assistant_* MCP. Each mission runs in an isolated container (workspace) with a chosen agent profile and a self-contained prompt. Use this skill whenever the user wants to 'launch a mission', 'sandboxed', 'spawn a worker', or delegate a multi-step coding/research task that should run in a clean environment.

Check whether Claude Code, Claude Agent SDK, and Codex SDK have new versions on npm. Use when the user asks to check for SDK/CLI updates, e.g. "check sdk updates", "check claude code update", "有沒有更新", "檢查 SDK 版本".

Mandatory for every Nomi corrective change: user-reported bugs, regressions, CI-only failures, flaky tests, performance or security defects, review/audit findings, and compatibility failures in any production path. Classify one_off versus recurring before implementation. Recurring and high-risk repairs require a schema-v3 contract, shared enforcement boundary, structural prevention, dependency lifecycle decision, and changed regression evidence.

品牌宣传片 playbook。把产品文案/卖点做成一条「3 秒钩子 → 卖点 → 使用场景 → 行动号召」的短宣传片，分阶段执行（剧本审阅 → 拆镜 → 落画布 → 生成 → 排时间轴），每阶段暂停让用户审阅。

编辑创作区文档：读取当前内容，追加或替换文本，维护分镜描述格式。

非对抗性动作场景（跑酷/追逐/攀爬/特技/坠落）的动作编排知识——专业动作词汇、环境交互、动作节奏、FPV 追拍、身体力学约束、怎么把动作精确物理化地描述进视频提示词；Nomi 写动作戏 shot 时参考。

服化道——设计「人物设定图」与「场景环境图」的生图提示词，含顶部「风格前缀块」（摄影机/胶片/调色/画幅等烧进画面的统一风格）+ 生图 avoid（多指/文字水印/穿帮等）+ identity DNA（角色跨图一致的关键特征锁定）。Nomi 为角色/场景等视觉 anchor（`carrier: visual`）生成参考图、写它的生图 prompt 时参考。

镜头语言与摄影技法方法论——统一景别体系/构图规则/运镜的情绪语言/打光方案/景深控制/色温光源/镜头特性，以及这些怎么翻译成视频提示词该怎么写。Nomi 拆镜头或写视频 shot 的 prompt 时参考。

治 AI 视频多镜头「同角色换脸 / 同道具换形 / 同场景换景」三大顽疾的方法论——五维一致性检查、参考图锚定、场内状态表、段间承接。Nomi 拆镜头与跨镜生成时参考：把每个跨镜元素落成画布 anchor 节点（角色/场景/道具/风格），镜头用 anchorIds 引用、系统连参考边，段间用首尾帧承接。

中国古装题材形制知识——服饰造型(朝代/身份/发冠/妆容/色彩等级)、建筑形制(屋顶等级/开间/斗拱/院落/衙门公堂)、礼仪调度、穿帮自检，以及古装参考图/视频提示词要点(古匾汉字放开、现代穿帮 avoid)；Nomi 做古装短片/微短剧时参考。

参考图（角色/场景设定图）的 Go/No-Go 审图清单——逐项检查 + 判定规则 + 反馈模板，帮用户快速识别设定图是否合格、不合格时怎么反馈调整重抽；Nomi 生成参考图后审核时用。

非武打文戏的表演指导——微表情/肢体语言/视线设计/沉默表演/角色行为模式。AI 不会自己「演」，情绪必须在 prompt 里精确物理化成可拍的身体信号（眉/眼/嘴/手/肩/呼吸/停顿）。Nomi 写文戏 shot 的 prompt 时参考，让画面演得出「情绪」而不是只写情绪词。

把「镜头意图」翻译成视频提示词的手册——运镜翻译表（推/拉/摇/跟/环绕…→提示词措辞）、焦点翻译、复合镜头(多 clip)模式、污染词替换铁律、把抽象动作/情绪换成物理动作。Nomi 写视频 shot 的 `prompt` 字段时参考。

声音设计知识——环境音/音效/配乐情绪/沉默设计/听觉记号（leitmotif）/声画对位，仅供叙事规划与后期剪辑参考；⚠️视频生成提示词不含音频，这些内容不写进 shot 的 prompt，是给 Nomi 时间轴后期配音/配乐用的。

多角色调度方法论——3 人以上同框的站位设计/视线网络/注意力引导/前后景分层/AI 生成中的多角色一致性控制；Nomi 写多人 shot 的 prompt（谁站哪/朝向/视线/谁是焦点）时参考，走应用内 Seedance/可灵生成。

导演风格融合招式·大友启史 × 埃德加·赖特（时尚动作怪盗 DNA）——按情节类型（飙车/打斗/跑酷/盗窃潜入/文戏温情/时尚换装登场）分别配运镜 + 剪辑节奏 + 声画卡点；Nomi 做高燃动作 / 时尚怪盗戏、想要这种质感时套用。运镜写进镜头 prompt，卡点节奏当时间轴剪辑参考，经应用内 Seedance / 可灵生成。

创意转场方法论。AI 视频逐条生成、帧天然对不齐，硬切会露缝——用遮挡/甩镜/入暗/匹配剪辑/物体擦过镜头等创意转场把相邻两条缝合成一气呵成，同时把转场当叙事工具用（场景切换/时间跳转/角色替换/情绪转折）。Nomi 里设计跨镜衔接、给相邻 shot 写首尾帧提示词时参考。

短剧 playbook。把故事梗概/小说片段做成一条有人物、有转折的剧情短片，分阶段执行（剧本审阅 → 立圣经+拆镜 → 落画布冻结定妆 → 参考图生视频 → 排时间轴），每阶段暂停让用户审阅。跨镜不换脸靠「先冻结角色卡、再从它生成」。

Connect HTTP models or a native ComfyUI workflow through Nomi's verified certification path.