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概要と使いどころ
Configures VPC endpoints (interface and gateway) for private AWS service access using AWS PrivateLink. Use when setting up secure private connectivity to S3, DynamoDB, and other AWS services without internet gateway, NAT device, or public IP addresses. Covers endpoint creation, security groups, route tables, and DNS configuration.
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。
Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
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。
Configure this skill should be used when the user asks about "API contract", "api-contract.md", "shared interface", "TypeScript interfaces", "request response schemas", "endpoint design", or needs guidance on designing contracts that coordinate backend and frontend agents. Use when building or modifying API endpoints. Trigger with phrases like 'create API', 'design endpoint', or 'API scaffold'.
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。
Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.
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。
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".
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。
Story structure for screenplays and stage plays (故事结构) — merged from Syd Field's paradigm and plot points, Blake Snyder's Save the Cat beat sheet and board, Robert McKee's Story (event/scene/sequence/act, inciting incident, progressive complications, crisis-climax-resolution, subplots, story triangle), Julian Hoxter's nine-beat model, Neill Hicks' three acts as attraction/anticipation/satisfaction, and Lu Jun's Chinese short-play structure (凤头猪肚豹尾, 起承转合, eight openings, eight endings, 入戏要快/脉络要清/高潮要高/转变要顺/变化要多). Use when outlining a script, placing plot points and midpoint, fixing a saggy second act, designing an opening or ending, diagnosing "nothing happens", choosing between linear and non-linear designs, or converting a premise into beats and cards.
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。
Practise the essay paper (申论) of the Chinese civil service examination (国考 / 省考): work a question from the given materials (给定资料), find the scoring points (要点), draft an answer in the expected format, and mark it the way examiners do. Use when asked 申论怎么写, 帮我批改申论, 归纳概括题怎么答, 申论大作文, 公文写作题, or practise the shenlun paper. Produces a question-type diagnosis, the points extracted from the materials with their source paragraphs, a model answer within the word limit, a marking of the person's own answer against the points, and a practice plan.
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。
Plan tests for an API endpoint or service — functional, negative, and contract. Use when asked to test an API, write API test cases, plan REST/GraphQL endpoint testing, or validate an API contract. Produces an API test plan — per-endpoint cases (status codes, schema, auth, validation, errors), boundary/negative cases, contract checks, and non-functional notes — so the API is verified beyond the happy 200.
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。
Save bounded progress as kernel.checkpoint/v1 for exact resume after reset. Triggers: checkpoint, save progress, context reset, compact soon, long task.
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。
Build the deterministic PIL pill overlays for a 'perfect-score + proof-points' UGC video ad (white 10/10 score header with a medal + orange sub with a finger-down + 3-4 green-check proof pills) and composite them onto a base clip in a diagonal L->R->L->R cascade, then mux the music bed into master-final.mp4. Config-driven (config.json), 1080x1920 9:16, FREE and deterministic (no paid calls, text stays pixel-crisp). Use for the overlay-proof-points format; the base clip + music come from separate paid capabilities.
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。
Test website responsiveness across viewport widths using browser automation. Resizes a single session through breakpoints, screenshots each width, and detects layout transitions (column changes, nav switches, overflow). Produces comparison reports showing exactly where layouts break. Trigger with 'responsiveness check', 'check responsive', 'breakpoint test', 'viewport test', 'responsive sweep', 'check breakpoints', or 'test at mobile'.
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 code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
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。
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.
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。
Produces an adversarial attack on a legal argument that survives reply. Runs the opposing-counsel discipline twice: an unrestrained first pass, then the same instrument turned on that pass to cut every point that collapses under challenge - attacks on conceded facts, wrong-forum objections, speculation about documents and motives, gotchas with innocent explanations, overclaims, self-refuting assertions, and padding. The deliverable is one standalone attack containing only what can be defended, capped at five heads, not a discussion of the discarded draft. Use to attack, stress-test, red-team or rebut a submission, brief, motion, witness statement, letter or structured legal reasoning. Triggers on "attack this but only with points that hold", "no cheap shots", "what survives reply", "which points can I actually defend", "give me the version I can file", "double-pass adversarial review". Also use when an earlier adversarial review came back overlong or scattershot. Formal British English.
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 single-point rubric with one criterion and open columns for evidence. Use for student self-assessment, peer feedback, teacher formative feedback, or pre-task planning. Works with any learning target, with or without a band system.
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 blueprint for player mortality loops: progress-index checkpoints, soul graves, ghost/spirit layers, I-frame restitution, world-progress persistence, and death analytics. Use when implementing respawn, shrine checkpoints, corpse-run retrieval, or second-chance mechanics. Keywords revival, respawn, checkpoint, soul grave, ghost mode, I-frames, death analytics, progress index.
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 Chase UR travel portal via Patchright for cash prices, points pricing, Points Boost offers, and Chase Edit hotel benefits. Use for pay-with-points portal comparison on Sapphire Reserve/Preferred.
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。
Use when a user wants Codex to directly operate PowerPoint and reconstruct an entire reference image as editable PowerPoint objects while preserving its full layout, labels, connectors, and visual relationships. Do not use for paper-to-deck generation or VBA-only output.
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。
Diagrams and checkable docs from a codebase. Draws ERDs, C4 context/container/component views, API endpoint catalogues, lifecycle/state machines, and service-to-service call maps as a validated JSON spec plus a standalone HTML viewer, with Mermaid export. Imports OpenAPI, GraphQL SDL and Prisma; otherwise reads the code (NestJS, TypeORM, Express) and writes the spec. Use for: database diagram, ERD, data model, table relationships, C4 or architecture diagram, system context, API map, endpoint list, "show me the endpoints", status flow, state machine, allowed transitions, "what happens after X is approved", how services call each other, microservice map, Mermaid diagram. Also writes documentation as a fact graph: each statement carries its source and a computed confidence, and a drift check fails CI when the code moves. Use to document a system, write architecture or onboarding docs, or explain how a system works.
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。
Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.
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。
Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.
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。
Generate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
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。
Appointedd integration. Manage data, records, and automate workflows. Use when the user wants to interact with Appointedd data.
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。
Appointo integration. Manage data, records, and automate workflows. Use when the user wants to interact with Appointo data.
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。