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概要と使いどころ
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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。
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
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15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing between server and client fetching, setting up caching strategies, or handling loading and error states.
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。
Provides patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).
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。
Runs parallel prose and craft review agents against a voice profile. Use when checking generated content for AI patterns and voice drift before publishing.
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。
Shapes agent behavior via instruction framing and style transfer. Use when composing dispatch prompts or writing skill instructions for parallel review agents.
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 a project skill library in .claude/skills/ via discovery, parallel authoring, and review. Use when packaging tribal knowledge. Do not use for one skill.
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。
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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。
Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.
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。
Uses Agent SDK to deploy 3 parallel agents for client onboarding -- workflow auditor, tech stack mapper, and strategy drafter. Real consulting workflow that produces a complete client assessment.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
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。
Operate Flash Linear Attention package workflows: setup, kernels, layers/models, KDA/context parallel, and benchmarking.
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 repo skill for Dask, the Python parallel computing library, when working with lazy task graphs, schedulers, Dask Array, Dask DataFrame, Dask Bag/bytes IO, configuration, diagnostics, CLI usage, or contributor validation.
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 DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.
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 ColossalAI for distributed PyTorch training, large-model parallelism, Booster plugins, ShardFormer, Colossal-Inference, and first-party application workflow routing.
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。
Autonomous sub-agent hand-off for a fiddly, fully-specified exploit-compile / escalation RUN - the main agent stays on strategy and the board while a cheap sub-agent runs an exact copy-paste checklist behind a false-root/hostname guardrail. Use for "delegate", "offload", "hand this to a sub-agent", "spin a haiku", or the moment a foothold plus a working escalation vector is identified. Main agent dispatches, waits (no parallel duplicate), integrates the result.
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。
You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.
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Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.
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。