「present」の検索結果

Read before a js eval cell that installs a package, spawns a server or PTY, or starts a long run, and before any bun -e, script, server, CLI, test, bundle, or package-management work: this session's eval js kernel runs Bun 1.4+ (this skill is present only when it does). Bun 1.4 ships builtins that replace 15+ npm deps — check here BEFORE installing sharp, puppeteer/playwright (scraping), marked, node-cron, node-pty, concurrently, serve-static, tar, json5, fast-xml-parser, string-width. Triggers: eval js, bun, Bun.serve, bun test, bun build, bun install, bun run, image resize, headless browser, markdown render, cron, PTY.

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 compelling data-driven narratives. Use when presenting analysis results, creating stakeholder reports, or transforming a set of findings into a story that drives a specific decision or action.

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。

Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data 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。

Read a real .pptx using text extraction, OOXML inspection, slide rendering, OCR, visual inventory, and vision-capable screenshot review; then write academic speaker notes grounded in every visible slide element, generate a complete display-version document, and inject clean notes into the PowerPoint notes pane. Use when the user wants speaker notes, presenter notes, a speech script, narration, or annotated notes for an existing PowerPoint deck, especially when slides contain images, charts, tables, SmartArt, axes, legends, or screenshot text.

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。

Browser-based HTML presentation generation.

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 images using Google Gemini's image generation capabilities. Use this skill when the user needs to create, generate, or produce images for any purpose including UI mockups, icons, illustrations, diagrams, concept art, placeholder images, or visual representations.

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。

This skill should be used when moving from design to implementation. Triggers on "let's build", "implement this", "looks good let's code", "ready to implement". Presents options for parallel agent competition (cookoff), single subagent, or local implementation. Each agent creates own plan from shared design for genuine variation.

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。

Presents options for merge, PR, or cleanup. Use when work is complete, tests pass, and you must decide how to integrate.

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 the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission. Triggers on phrases like "reviewer questions", "anticipate reviewers", "rebuttal", "paper weaknesses", "defend the paper", or "strengthen the paper".

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。

Automatic adversarial review gate that spawns 3 independent reviewers in parallel after any plan is drafted - all must PASS before presenting to user

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。

Fetch and rank open GitHub issues by community engagement, present the top 3 candidates, and plan implementation for the selected issue. Use when the user asks to "pick next issue", "next issue", "which issue should I work on", "top issues", "most popular issues", "prioritize issues", or "what should I work on next".

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。

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

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 this skill when stuck in circular debugging, when solutions aren't working despite multiple attempts, or when the user expresses frustration with lack of progress. Bring in GPT-5 as a third-party consultant to provide fresh perspective on complex technical problems, architectural decisions, or multi-system debugging issues. Ideal when you've tried multiple approaches without success, when the problem involves obscure edge cases or novel challenges, or when a second expert opinion would help break through an impasse. Gather all context from the conversation so far and present it comprehensively to GPT-5.

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 implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

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。

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

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 lens critique covering visual hierarchy, clarity, and anti-slop patterns — produces a findings table, no code edits unless asked. Use when the user wants a design review, says "what's wrong with this UI", or needs a second opinion before a handoff or presentation. Invoke when the user asks for critique on their UI, or mentions 'critique' alongside design / UI / frontend work.

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。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

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。

GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.

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。

PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.

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。

Scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design, talk structure, timing, data viz for slides, QA. PowerPoint and LaTeX Beamer. For posters use latex-research-posters.

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。

De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat). Use after MACS3 to identify enriched TFs, annotate peaks with nearest genes, and validate ChIP-seq via the target motif.

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 a self-contained HTML slide deck from a topic, outline, report, or data. Use for presentations and slide decks; not for editing PowerPoint files.

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。

Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows, publication-ready matplotlib house style, and layout using routed visual-evidence checks. Use when checking or creating plot readability, axes, legends, line overlap, table structure, metric reporting, captions, grayscale accessibility, IEEE one-column/two-column layout, figure/table placement, claim-evidence mapping, reviewer first-impression risks, matplotlib/seaborn or ggplot2 figure code, SVG/PDF/TIFF/PNG export, Illustrator/Inkscape/PowerPoint/Figma finishing plans, or IEEE-style engineering evidence graphics.

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。

SST v4 (Ion) expert for managing AWS resources as code with the Pulumi-backed framework. Use when writing or editing sst.config.ts, building infra/ modules (sst.aws.Function/Bucket/Dynamo/Cron/Service/Router, sst.Secret, sst.Linkable, raw aws.* Pulumi resources), wiring resource links, scoping IAM, or running sst deploy/dev/diff/remove. Essential when the user mentions SST, sst.config.ts, $config, $transform, $interpolate, sst.aws.*, sst.Secret, Pulumi/Ion, "sst deploy", a failed SST deploy (ConflictException on a resource-type change, "Identifier '__filename' has already been declared", MalformedPolicyDocument on an Output<T>), or wants to scaffold/troubleshoot AWS infrastructure with SST. Also use when a request to "deploy my AWS stack" or "add a Lambda/bucket/table" is made in a repo that already contains an sst.config.ts (using $config) or an sst dependency. Do NOT use when the task is primarily AWS CDK, Terraform, raw CloudFormation, or SAM with no SST present — those have their own tooling.

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。