「structure」の検索結果

Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.

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。

Prompt and workflow for generating conventional commit messages using a structured XML format. Guides users to create standardized, descriptive commit messages in line with the Conventional Commits specification, including instructions, examples, and 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。

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.

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。

Agents workbench layout — covers the fixed layout structure, grid configuration, part visibility, editor modal, titlebar, sidebar footer, and implementation requirements. Use when implementing features or fixing issues in the Agents workbench layout.

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。

Atom of Thoughts (AoT) reasoning - decompose complex problems into atomic units with confidence tracking and backtracking. For genuinely complex reasoning, not everyday questions. Triggers on: atomise, complex reasoning, decompose problem, structured thinking, verify hypothesis.

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Expert GNU awk (gawk) one-liner generation and transformation. Use this skill whenever the user asks to process text fields, extract columns, compute sums or aggregates, filter structured output, reformat CSV/TSV, parse logs, generate reports, or do any record/field processing task. Trigger on keywords like "awk", "extract column", "sum a field", "count occurrences", "print field N", "filter by column", "reformat output", "aggregate log data", "process CSV", "TSV", or any request that maps to a field-oriented text transformation. When in doubt, use this skill — awk is often the right tool when sed isn't enough.

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Patterns for designing well-structured, maintainable Ansible playbooks with state-based (present/absent) design, play organization, and proper variable scoping.

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Production-grade Ansible role structure patterns for directory layout, variable organization (defaults vs vars), handlers, and task organization, derived from analysis of 7 geerlingguy production roles.

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Adopt a virtual Malinois dog at animalhouse.ai. Working dog. Needs discipline and structure or it self-destructs. Feeding every 3 hours. Rare tier dog.

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5 proven agent skill design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) from Google's ADK. Build reliable, composable skills with templates, decision trees, and composition guides. Stop cramming logic into prompts—use structured patterns instead.

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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。

Human verification for AI agents. Submit claims, draft responses, or observation requests to human domain experts via the 2O API. Returns structured verdicts with confidence scores and evidence.

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 (1) building an AI agent that needs to communicate with other agents over the Agent2Agent (A2A) protocol, (2) publishing an AgentCard for discovery, (3) acting as an A2A client or server, or (4) integrating A2A capabilities into an existing agent framework. Solves agent isolation — enabling discovery, capability negotiation, task delegation, and structured data exchange across different frameworks and vendors.

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Build next-session A-share game plans from market structure, overnight macro shocks, policy timing, and watchlist leadership. Use when the user asks what A-shares may do tomorrow, which sectors may repair first, how to read the open, or wants a reusable pre-open discretionary decision workflow.

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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

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Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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Author in-repo SKILL.md: frontmatter, validator, structure.

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Dark-themed SVG architecture/cloud/infra diagrams as HTML.

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Perform a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the work is functionally complete and needs finishing touches—not when the hierarchy, structure, tone, or technical foundation still need major changes.

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Improve layout composition, spacing systems, grouping, and visual rhythm. Use when the user mentions weak layout structure, arbitrary spacing, weak grouping, crowded composition, or layout monotony rather than priority/emphasis problems.

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Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.

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Simplify cluttered UI by removing unnecessary elements, choices, features, and structural complexity. Use when the problem is too many controls, options, features, or competing elements—not when the structure is sound and the real issue is only visual intensity or lack of personality.

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。

Reduce visual intensity, saturation, and noise while preserving hierarchy and character. Use when the structure is basically sound but the design feels too loud, bold, garish, aggressive, or overstimulating—not when the real problem is structural clutter, too many features, or not enough personality.

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。