「natural」の検索結果

Evaluates environmental liability exposure with remediation cost estimation, regulatory compliance requirements, and insurance coverage assessment. Use when analyzing environmental liabilities, estimating cleanup costs, or assessing environmental risk.

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Semantic code search using natural language queries. Use when users ask "where is X implemented", "how does Y work", "find the logic for Z", or need to locate code by concept rather than exact text. Returns file paths with line numbers and code snippets.

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The logical structure of mathematical proof -- how first-order logic, natural deduction, and semantic reasoning combine to produce rigorous mathematical arguments. Covers the logical skeleton of direct proof, contraposition, contradiction, cases, and induction; the role of definitions, lemmas, and theorems; how informal mathematical prose maps to formal deductive structure; and the difference between proof-as-object and proof-as-activity. Use when the question is "why is this a valid proof?" rather than "what proof technique applies?"

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Practical argument analysis and construction -- how to identify, evaluate, reconstruct, and build real arguments in natural language. Covers argument identification (premises, conclusions, intermediate steps), charity and steel-manning, argument mapping, the distinction between deductive validity and inductive strength, burden of proof, and the rhetorical context in which arguments operate. Use when the goal is to engage with real-world argument rather than to formalize it.

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Extending propositional and predicate logic with modal operators -- necessity and possibility, and their relatives (obligation, knowledge, belief, time). Covers Kripke possible-worlds semantics, accessibility relations, the main modal systems (K, T, S4, S5), translation from natural language, and applications in epistemic, deontic, and temporal reasoning. Use when ordinary logic is insufficient to capture distinctions like "must" vs "might," "knows" vs "believes," "always" vs "sometimes."

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The logic of sentential connectives -- and, or, not, if-then, if-and-only-if -- as a formal system. Covers syntax (well-formed formulas), semantics (truth tables), satisfiability, validity, logical equivalence, normal forms (CNF, DNF), natural-deduction rules, and common translation pitfalls. Use when formalizing arguments with propositional structure, checking validity mechanically, or teaching the foundations of formal reasoning.

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Earth systems science covering plate tectonics, landforms, erosion, biomes, hydrosphere, atmospheric circulation, and the interactions among lithosphere, hydrosphere, atmosphere, and biosphere. Provides frameworks for analyzing how physical processes shape landscapes over geological and human timescales. Use when reasoning about landforms, natural hazards, climate patterns, ocean currents, biogeography, or any question about how Earth's physical systems operate and interact.

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Climate physics, forcing, feedback, and attribution. Covers the greenhouse effect and radiative forcing, climate sensitivity, fast and slow feedbacks, the paleoclimate record, detection and attribution science, regional climate impacts, and the distinction between weather, climate variability, and forced climate change. Use when reasoning about global warming mechanisms, interpreting temperature or CO2 records, evaluating attribution claims for extreme events, or distinguishing signal from natural variability.

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Remove AI-isms and artificial language patterns from text. Makes documentation, comments, commit messages, and prose sound more natural and human. Based on Wikipedia's "Signs of AI writing" patterns.

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Bria.ai image API — generate from text prompts, edit with natural language, remove backgrounds for transparent PNGs, and create product lifestyle shots. Authenticates via OAuth device flow, caches credentials in ~/.bria/credentials, calls 20+ endpoints. Commercially safe, royalty-free. Triggers on: remove background, transparent PNG, cutout, generate image, create banner, edit photo, product photography, upscale, restyle, inpainting, outpainting, lifestyle shot, background replacement, object removal, photo restoration.

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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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Formal oracle for abductive inference that selects the minimal hypothesis best explaining a set of observations. Provides three sub-oracles: MCMC via monad-bayes, Gemini for natural language, and propagator networks for constraint domains. Use when performing hypothesis selection, explaining observations, or solving inference-to-best-explanation problems.

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Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.

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Create or update the feature specification from a natural language feature description.

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Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.

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Detect AI/LLM-generated text patterns in research writing. Use when: (1) Reviewing manuscript drafts before submission, (2) Pre-commit validation of documentation, (3) Quality assurance checks on research artifacts, (4) Ensuring natural academic writing style, (5) Tracking writing authenticity over time. Analyzes grammar perfection, sentence uniformity, paragraph structure, word frequency (AI-typical words like 'delve', 'leverage', 'robust'), punctuation patterns, and transition word overuse.

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Transform any research material into SEO+GEO-optimized blog posts for Knox Hub. Takes any input (URL, article text, notes, video transcript, competitor post, topic idea) and rewrites it as natural human-sounding content, then fills in all fields for the Knox Hub post editor: title, excerpt, SEO settings, FAQ schema, table of contents, sticky sidebar, and full article content in Markdown. Output is ready to copy-paste into your blog/CMS post editor. Use when asked to "write a post", "rewrite this", "сделай пост", "напиши статью", "перепиши это для блога", "create blog post", or any content creation task.

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Write a full YouTube video description under 5,000 characters with a first-150-character hook (the only part visible before Show more, and what shows in search and suggested), timestamped chapters, naturally placed keywords, links, and a clear call to action. Covers long-form and YouTube Shorts. Becomes the create-post content that publishes with the video. Use to write or rework a description. Not for the title (use yt-title-optimizer) or the spoken opening (use yt-hook-scripter).

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generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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Rewrite AI-generated text to sound natural and human-written

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Use browser-use to turn natural-language web tasks into repeatable browser automation backed by Playwright and agent loops. This skill helps an agent open sites, inspect page state, click, type, extract data, and recover from common UI changes with a real automation framework instead of brittle one-off scripts.

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Use Agent QA's CLI and MCP server to author, validate, run, debug, and triage natural-language web and mobile regression tests with persistent test memory and reviewable run evidence.

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mcp-atlassian is a Model Context Protocol server that connects AI assistants to Atlassian Jira and Confluence. It enables searching and managing Jira issues, reading and editing Confluence pages, and performing project management tasks through natural language via any MCP-compatible client.

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The Atlassian Rovo MCP Server bridges your Atlassian Cloud workspace with any MCP-compatible client. Search and summarize Jira issues, create tickets from natural language, update Confluence pages, and query Compass services.

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