「structure」の検索結果

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, PRDs, RFCs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

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Extract, define, and apply design DNA across three dimensions: design system (tokens), design style (qualitative feel), and visual effects (Canvas, WebGL, 3D, particles, shaders, scroll effects, etc.). Use this skill when: (1) a user wants to see the full 3-dimension design structure/schema, (2) a user provides images, screenshots, or URLs of reference designs and wants them analyzed into a structured JSON profile covering all three dimensions, (3) a user has a Design DNA JSON and content and wants a design generated from it, or (4) any combination of these phases. Triggers on "design DNA", "extract design style", "analyze design", "design tokens from reference", "generate design from JSON", "design system from screenshot", "design profile", "style guide JSON", "visual effects analysis", "design with effects", "3d design analysis".

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Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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Reorganizes a paper into a storyline suitable for scientific posters. Use when planning the section structure, title hierarchy, figure selection, and live-explanation flow for an academic conference poster. Also triggers on "help me design a poster layout", "what sections should my poster have", "how do I arrange my poster", "poster structure for [conference]", or "which figures should I use for my poster".

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Organizes biomedical figures, analyses, and result blocks into a clear Results section structure with disciplined narrative ordering and evidence-aware presentation.

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Build and query suffix arrays and related structures

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Select optimal data structure based on operation requirements

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Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

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Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.

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Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

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Methodology for clause-by-clause review of a contract against a structured deviation policy ("playbook"). Covers how to walk a playbook, locate the matching provision in the contract, apply rule types (max-value, must-be-present, must-be-absent, acceptable-set, must-have-feature), classify the result (ok / risk / reject), choose the prescribed action, and ground each finding in a verbatim excerpt. Use whenever reviewing any contract — NDA, MSA, vendor DD questionnaire, lease, DPA — against a structured rules-based playbook.

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 before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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。

Analyzes and optimizes content structure including header hierarchy, suggests schema markup, and internal linking opportunities. Creates search-friendly content organization. Use PROACTIVELY for content structuring.

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Expert Terraform/OpenTofu specialist mastering advanced IaC automation, state management, and enterprise infrastructure patterns. Handles complex module design, multi-cloud deployments, GitOps workflows, policy as code, and CI/CD integration. Covers migration strategies, security best practices, and modern IaC ecosystems. Use PROACTIVELY for advanced IaC, state management, or infrastructure automation.

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Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

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Build or restructure a course syllabus from a topic list or reading list — course description + prerequisites, week-by-week schedule (topic → readings → deliverables), measurable learning objectives, an assessment scheme + rubric, standard policies (late work, AI use, academic integrity, accessibility), and a per-week work-list to hand to `/create-lecture`. Use when user says "build a syllabus", "structure my course", "turn this reading list into a schedule", "draft a course outline", "make a syllabus for Econ 7xx", or "map weeks to lectures". Economics-aware (PhD metrics/micro/macro sequences, undergrad); generic enough for any field.

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Distill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".

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Case studies of American screenwriting (美国电影剧作案例) — Mei Feng's readings of the Hollywood Western (value pairs, four oppositions), screwball comedy (strong heroine, buddy vs adversary two-hander), Billy Wilder (disguise, Lubitsch touch, meet-cute), Hitchcock and his writers, Adaptation, Mildred Pierce (eight drafts, woman's film + noir), Thelma & Louise ("but" method, mirror pair), Fargo (Coen method), Good Will Hunting (five-part structure), American Beauty (POV strategy, genre mix), Eternal Sunshine (1:5:1, image motifs); Walter's American Graffiti; the Hollywood masterclass films (Bourne, Guardians, Zootopia, Thor); Hoxter's Winter's Bone and Jojo Rabbit; Snyder's Miss Congeniality; Field's Chinatown. Use when you want a worked example of how a specific American film's script is built, when analysing a film for structure, character or theme, or when looking for a model to imitate for a Western, screwball, noir, buddy, coming-of-age, black comedy or non-linear romance.

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Project orchestrator for the screenwriting skill set (剧本项目主线调度 / 状态存档 / story bible) — a meta-skill that adds no new craft knowledge but routes a screenplay or stage-play project through stages (premise → structure → character → scenes → draft → revision → submission) and a TV / streaming series project (电视剧 / 剧集 / pilot / 一季) through a separate series stage table (engine → character network & season arc → documents/bible → pilot structure → break story & outline → draft → submission), names which sw-* skill to invoke at each stage, defines each stage's deliverable and advisory exit check, and keeps all project state in a story-bible.md file so work can resume across sessions. Use when starting a new script project, resuming one ("continue my screenplay", "where were we"), when the user asks "what should I do next" on a script, when converting a vague idea into a full development pipeline, or when the other sw-* skills are firing individually and the overall process needs sequencing.

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Writing plays and screenplays in the Chekhovian mode (契诃夫戏剧法) — distilled from the complete plays (没有父亲的人/普拉东诺夫, 伊凡诺夫, 林妖→万尼亚舅舅, 海鸥, 三姊妹, 樱桃园, nine one-acts) plus Chekhov's own letters, Stanislavsky's and Nemirovich-Danchenko's rehearsal notes and 焦菊隐/童道明 criticism. Covers the four-act mood-based structure without central climax, off-stage events, "correct answer stated in Act 1 and refused", the announced event that never happens, three-layer endings (hope monologue + nihilist line + household action), no-villain conflict with environment, every character's object and catchphrase, one-way love chains, half-step-off dialogue, philosophy cut by thirst, the shortest words for the deepest pain, stage-direction and sound discipline, and how Chekhov revised The Wood Demon into Uncle Vanya. Use when the user wants an ensemble drama, a "nothing happens" play, subtext-heavy dialogue, a family estate / provincial story, an anti-climactic structure, or asks how to "Chekhov-ize" a script.

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Draw cloud architecture diagrams for AWS, Azure or GCP with the official provider icon sets, using TerraVision. Use whenever the user asks to draw, diagram, sketch or visualize a system on AWS, Azure or Google Cloud, or one built from their services (Lambda, DynamoDB, S3, EC2, EKS, RDS, API Gateway, Azure Functions, App Service, AKS, Cosmos DB, Cloud Run, GKE, BigQuery and the like), even if they never say 'cloud', 'diagram' or a tool name; also for a diagram of Terraform code. Prefer it over Mermaid, PlantUML, hand-written SVG or ASCII, which cannot use the official icons or VPC/subnet grouping. Not for diagrams that are not cloud infrastructure: sequence diagrams, flowcharts, class or ER diagrams, code or module structure, org charts, on-premises-only networks; use Mermaid or similar for those. Works with or without Terraform: with Terraform code the diagram comes from terraform plan; without it, write a small JSON graph and render it.

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AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

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Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

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Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

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。