「sla」の検索結果

A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \"analyze this paper\", \"critique this study\", \"is this a strong paper?\", \"give me similar studies\", \"prepare me for journal club\", \"help me understand this bioinformatics paper\", \"what are the weaknesses here?\", or \"turn this into a mind map\". Also activate for any downstream deliverables such as journal club kits, comparison tables, PI decision briefs, replication starters, or follow-up experiment designs. Do NOT treat as a generic summarizer — this skill performs structured evidence-type classification, track-specific critical appraisal, interpretation-boundary judgment, and research-grade follow-up 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。

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

Rewrites technical research content into a structured lay summary that cross-disciplinary teams can quickly understand and act on. Use when the user wants to explain research to colleagues outside their specialty — clinicians, wet-lab scientists, bioinformaticians, product managers, or leadership. Trigger on: "lay summary", "explain my research to the team", "non-technical summary", "cross-disciplinary summary", "translate my findings", "align our team on the study", or any request to communicate research goals, findings, or next steps to a mixed or non-specialist audience. Part of the AIPOCH Academic Writing skill hub. Sits midstream: after research content is clarified, before downstream deliverables like slide decks or graphical abstracts.

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 your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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。

Local drop-in API emulator for Vercel, GitHub, Google, Slack, Apple, Microsoft, AWS, Clerk, Linear, Twilio, and other developer APIs. Use when the user needs to start emulated services, configure seed data, write tests against local APIs, set up CI without network access, or work with the emulate CLI or programmatic API. Triggers include "start the emulator", "emulate services", "mock API locally", "create emulator config", "test against local API", "npx emulate", or any task requiring local service emulation.

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。

Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image 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。

Triage Slack issue reports with one thread-only verdict, evidence review, cause-aware routing, tracker dedupe, and fail-closed ticket creation. Use only from the configured Benny triage automation.

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 Benny and prepare its triage and repro automations. Use when installing Benny or changing its Slack, tracker, repository, routing, control, model, or budget settings.

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。

Reproduce triaged Slack bugs through a configured app-control adapter, verify existing fixes, and open a bounded draft pull request only after before-and-after proof. Use only from the configured Benny repro automation.

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。

Guide for translating Python classes, inheritance, and object-oriented patterns to Scala. Use when converting Python code with classes, dataclasses, abstract classes, inheritance, properties, static methods, class methods, or design patterns.

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。

Guide for translating Python collection operations to idiomatic Scala. Use when converting Python code that uses lists, dictionaries, sets, or involves collection transformations like map, filter, reduce, sorting, and aggregations.

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。

Reference guide for translating Python syntax constructs to Scala equivalents. Use when converting Python code to Scala and need mappings for basic syntax elements like variable declarations, control flow, comprehensions, string formatting, and common operators.

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。

Guide for translating Python code to functional Scala style. Use when converting Python code involving higher-order functions, decorators, closures, generators, or when aiming for idiomatic functional Scala with pattern matching, Option handling, and monadic operations.

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 simulating quadrotor physical dynamics — mapping desired thrust/moments to individual motor RPMs via a propeller allocation matrix, applying first-order motor lag, and integrating the nonlinear equations of motion (translational and rotational) using RK45.

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。

Translate logistics and operations rules into optimization variables and constraints. Use when an operations problem describes vehicles, routes, depots, pickups, dropoffs, inventory, capacity, assignments, time windows, service targets, penalties, resource limits, or other business rules that need to become an optimization model.

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。

Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs

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。

Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.

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 a visual direction — an archetype (high-end agency, editorial minimal, brutalist, soft-SaaS, dark-tech) or one of 138 named design systems (apple, linear-app, stripe, vercel, notion, material, shadcn, spotify, tesla…) — by resolving it into the token system. Use when the user wants a specific look/vibe/brand feel, or asks to make a design feel premium/expensive/non-generic.

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。

Für Versicherung Avb: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Erkennt Fehlerkatalog: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Versionsdiff AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Verjährungsverkürzung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: verjaehrungsverkuerzung.

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。

Für Vollmacht Vertretung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Schaltet sich ein, wenn in Aktenaufbereiter Strafrecht ein juristisches Arbeitsprodukt tragfähig begründet werden muss; verbindet konkrete Aktenfundstellen mit Tatbestandsmerkmal, Beweislast, stärkster Gegenposition und Rechtsfolge.

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。