2.7K skills · sorted by relevance
Memory consolidation pass: orient on existing memories, gather signal from logs/transcripts, merge updates into topic files, prune index. Use on 'dream', 'consolidate memory', 'clean up memories', 'prune memories', 'memory maintenance', or when memories feel stale.
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A hard-edged writing style contract for timeless, forceful English prose without modern AI tics. Use when users ask for prose or rewrites that must be clean, exact, concrete, and free of AI cadence, filler, or therapeutic tone.
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Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.
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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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クラウドルーチン（CCR / スケジュールエージェント）を作成し、作成直後に claude.ai が 自動付与する不要な MCP connector を判定して除去する一連の処理。 「ルーチンを作って」「スケジュールエージェントを作成」「routine 作成して connector を整理」等で使う。 明らかに不要な connector は確認なしで外す。作成は /schedule のスキーマに従う。
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Systematically evaluate weak, stale, or low-conviction theses for upgrade, monitoring, or closure. Use when user says "prune", "kill list", "clean up theses", "what should I close", or "reduce portfolio".
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Read-only sanity-check of EcoSIM plant_trait.*.desc parameter values for the first grid. Use when validating plant trait description files, enforcing clean C3/C4 physiological parameterization, reviewing photosynthetic kinetics, active leaf/root protein allocation, and root hydraulics such as RSRR, RVSR, and ARSRA, or checking ranges, units, and woody/herbaceous block consistency before running EcoSIM. Never edit or replace the `.desc` file.
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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.
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Operate and maintain OpenViking server - configure, install, start, stop, and cleanup the server. Use when need to setup or manage OpenViking service deployment.
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Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"
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Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
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Warm editorial, print-leaning document design system — parchment background, single ink-blue accent, serif-led hierarchy (LXGW WenKai for Chinese, Charter for English). For résumés, reports, white papers, letters, and editorial reading pages that should feel authored and published. Not a product-UI / component system.
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Shadcn/ui-inspired design with minimal, clean components, monochrome palette, and utility-first patterns.
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Straightforward, no-frills design with clean typography, neutral colors, and intuitive layouts that stay out of the way.
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Modern minimalist aesthetic with clean lines, intentional color palette, subtle interactions, and consistent spacing.
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Generous whitespace, consistent padding, and grid-based layouts for clean, readable, and breathing interfaces.
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Paper-textured, print-inspired design with minimal colors, clean serif/sans typography, and tactile surface qualities.
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Clean, high-contrast enterprise design for data-driven workflows with intuitive drag-and-drop patterns and structured layouts.
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Stripped-back design emphasizing whitespace, clean typography, and restrained color for maximum clarity and focus.
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Contemporary editorial style with serif typography, minimal palettes, and clean layouts for polished digital products.
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