「nim」の検索結果

Generates complete cross-disease shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Always use this skill whenever a user wants to design, plan, or build a multi-dataset study linking two related diseases through shared DEGs, enrichment, PPI hub genes, public validation, regulatory-network analysis, immune infiltration, drug-gene interaction screening, and optional qRT-PCR or cell-line validation. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, regulatory-network interpretation, immune/drug follow-up, bioinformatics-plus-validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.

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Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Generates complete single-compound network-toxicology research designs from one exposure, one disease or toxic phenotype, and a validation direction. Use when a study centers on one compound–one disease link and needs target collection, overlap construction, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-check, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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 complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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 complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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 complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO sensitivity analyses, leave-one-out testing, heterogeneity / pleiotropy checks, and multiple-testing control with FDR. Covers five study patterns (single-family bidirectional MR, multi-phenotype screening MR, subtype-resolved MR, phenome-style bidirectional causal map, mechanism-prioritized MR follow-up) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version...

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Use when running performance benchmarks, establishing baselines, or validating regressions with sequential runs. Enforces 60s minimum runs (30s only for binary search) and no parallel benchmarks.

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。

Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.

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Use this skill when the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.

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。

Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE between simulated and observed temperatures.

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。

Vercel's comprehensive UI guidelines for building accessible, performant web interfaces. Use this skill when reviewing or building UI components for compliance with best practices around accessibility, performance, animations, and visual stability.

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Use for day-ahead or multi-period unit commitment problems, including thermal on/off schedules, dispatch, startup/shutdown logic, minimum up/down time, ramping, spinning reserve deliverability, renewable curtailment, operating-cost accounting, and independent feasibility checks for power-system operations schedules.

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。

Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.

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 producing a minimally changed repaired 2D layout in JSON from extracted architectural geometry and rule-based clearance constraints.

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。

SetFit few-shot learning for efficient intent classification with minimal 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。

Parallel design review by 6 specialist agents (PM, Architect, Designer, Security Design, UX, CTO) with mandatory unanimous approval.

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。

Minimal, pattern-matching code output. Write the least code that satisfies requirements. Match existing project patterns. Use Write/Edit tools only.

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。

Red-Green-Refactor TDD methodology with mandatory failing tests, minimal implementation, quality refactoring, and 80% coverage gating.

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 implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

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。

Implement the minimal code needed to make failing tests pass in the TDD green phase.

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Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.

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。

Master Flutter development with Dart 3, advanced widgets, and multi-platform deployment. Handles state management, animations, testing, and performance optimization for mobile, web, desktop, and embedded platforms. Use PROACTIVELY for Flutter architecture, UI implementation, or cross-platform features.

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。

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

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。