「poi」の検索結果

Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause analysis.

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。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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。

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Record at least one captioned demo for every covered CUJ through its real entry point.

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。

Document a module or public API surface (functions, classes, CLI commands, endpoints) from the code itself. Use when the user asks for API reference, to document a module, or to write usage docs for a public interface.

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。

Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs. new-session composer, terminal strip vs. agent terminal, all twelve native harnesses). Covers the web UI, native harness terminals, and the CLI.

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。

Turn a range of commits or merged PRs into a changelog entry grouped by change type. Use when the user asks for release notes, a changelog, or "what changed" between two points.

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 and deploy an MCP server from an OpenAPI / Swagger spec using the mcp-use TypeScript SDK. Use this skill whenever the user wants to "turn this OpenAPI spec into an MCP server", "make this API usable from Claude/ChatGPT", "wrap this Swagger doc as MCP tools", "expose this REST API to an LLM", "generate MCP tools from a spec", or pastes/attaches an `openapi.yaml`, `openapi.json`, or `swagger.json` and asks for a Claude-compatible version. Trigger even if the user doesn't say "MCP" — if they describe an existing HTTP API (REST endpoints, an internal service, a third-party API they have a key for) and want an LLM to call it, this is the right skill. Covers spec ingestion (file path, URL, or pasted), operation-to-tool mapping, auth wiring (apiKey, bearer, basic, OAuth bearer), scaffolding with `create-mcp-use-app`, tool generation with proper zod schemas, live testing in the mcp-use inspector, and deploying to Manufact / mcp-use cloud.

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 or extend a course outline in your format, from class notes and casebook. Scaffolds — it does not write the outline for you. Use when the user says "outline [subject]", "add to my outline", "build an outline from", or points at class materials.

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。

First draft of a common clinic document — practice-area templates (asylum applications, eviction answers, protective order petitions, demand letters), jurisdiction-aware formatting, explicitly a starting point requiring student analysis and attorney review. Use when a student needs a first draft of a motion, letter, petition, declaration, or other clinic document.

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。

Prep for a cold-call — predict the professor's likely questions and drill them Socratically, flagging where you're shaky so you know what to re-read before class. Use when the user says "prep for class tomorrow", "cold call [case]", "what might [professor] ask on", or points at assigned reading.

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。

Read VDR documents and extract issues per house categories and materiality thresholds, producing findings in house memo format. Use when user says "review the data room", "extract issues from [folder]", "diligence review", "what's in the VDR", or points at VDR documents.

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 developer handoff specs from a design. Use when a design is ready for engineering and needs a spec sheet covering layout, design tokens, component props, interaction states, responsive breakpoints, edge cases, and animation details.

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。

Point a domain you already own at your Convex app (DNS records, custom-domain attach, auth-origin rebind).

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。

Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.

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。

Verify output format, file coverage, and task alignment before completing

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。

Validate PowerPoint files using python-pptx when read_file fails

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。

Robust PowerPoint generation with shell_agent primary approach and python-pptx fallback, including working directory verification and inline error debugging

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。

Validate PowerPoint files using python-pptx when standard file readers fail

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。

Robust PowerPoint generation with shell_agent primary and python-pptx fallback, including directory verification and error debugging

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 PowerPoint files using shell_agent and locate them in nested workspace directories

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。

Resilient PowerPoint generation with shell_agent primary path and python-pptx fallback

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。

Verify PowerPoint presentation contents using python-pptx via shell when standard file readers fail

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。