'llm' 검색 결과

Runs Docker Desktop and its WSL2 layer, checking RAM and disk before container work. Trigger when Docker Desktop or a container won't start, vmmem eats RAM or the Docker disk keeps growing; to set a WSL memory cap in .wslconfig; to list or install WSL distros; to bring a compose stack up or down; to prune images and build cache; to run a command in a container; to log in to a registry; or say dockerrunner (audit, check, up, down, prune, status, refresh). Local LLMs (Docker Model Runner too) are lmstudiorunner's; images comfyrunner's; VMs and sandboxes hypervrunner's; Docker's MCP catalog its MCP Toolkit's; registry tokens keywarden's.

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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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Design and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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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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Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

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Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks

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Lists all inference providers offered during NemoClaw onboarding. Use when explaining which providers are available, what the onboard wizard presents, or how inference routing works. Trigger keywords - nemoclaw inference options, nemoclaw onboarding providers, nemoclaw inference routing, switch nemoclaw inference model, change inference runtime, nemoclaw local inference, ollama nemoclaw, vllm nemoclaw, local model server, openai compatible endpoint.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.

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AI 视频生成全流程：通过 6 个阶段（剧本→角色/场景设计→分镜→参考图→视频生成→后期剪辑）将用户想法转化为完整视频。支持临时工作台（单独调用 LLM、VLM、文生图、图生图、视频生成）。触发词：视频生成、AI视频、AIGC、创作视频、制作视频、AI画图。

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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

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A Vedic astrology (Jyotish) analyzer based on the Indian Jyotish tradition, using the Sidereal zodiac to read birth charts. Covers 27 Nakshatras (Lunar Mansions), the Vimshottari Dasha (Major Period) system, and the Navagraha (Nine Planets). Triggered when users mention "Vedic astrology," "Jyotish," "Vedic," "Nakshatra," "Dasha," or "Indian astrology." Pure LLM reasoning for interpretation (precise charts require an astronomical engine). No external API required. Not applicable for: Western astrology horoscopes, BaZi, tarot, Zi Wei Dou Shu, or other domains → use the fortune-hub router instead.

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奇门遁甲排盘分析器，基于洛书九宫四层盘面体系（天盘九星、地盘三奇六仪、人盘八门、神盘八神）， 支持时家奇门排盘，用于择时决策和事项预测。当用户提及「奇门遁甲」「奇门」「遁甲」「择时」 「奇门排盘」「奇门预测」时触发。排盘为确定性计算 + LLM 综合解读，无外部 API 依赖。 不适用于：梅花易数、六爻易经、风水分析、八字命理、塔罗占卜等其他领域 → 建议使用 fortune-hub 路由。

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六爻易经占卜器，基于京房纳甲体系，模拟铜钱起卦，完成纳甲装卦（天干地支、世应、六亲、六神）， 通过用神旺衰和生克制化断卦。当用户提及「六爻」「易经占卜」「铜钱起卦」「纳甲」「帮我起一卦」 「六爻预测」时触发。装卦为确定性计算 + LLM 综合断卦，无外部 API 依赖。 不适用于：梅花易数、奇门遁甲、星座运势、塔罗占卜、八字命理等其他领域 → 建议使用 fortune-hub 路由。

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梅花易数占卜工具，属于 fortune-telling-skills 运势测算套件。基于邵雍《梅花易数》体系， 支持时间起卦、数字起卦、文字起卦等多种起卦方式，通过体用五行生克关系判断吉凶。 起卦为确定性计算 + LLM 断卦解读，无外部 API 依赖。 触发词：梅花易数、起卦、占卜、卦象、体用、八卦占卜、帮我起一卦、算一卦。 不适用于：星座运势、塔罗占卜、八字命理 → 建议使用 fortune-hub。

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风水分析顾问，融合八宅风水和玄空飞星两大理气派体系，通过出生年份计算本命卦， 结合房屋坐向排布飞星盘，给出吉凶方位和空间布局建议。当用户提及「风水」「八宅」 「飞星」「本命卦」「方位布局」「家居风水」「办公室风水」时触发。 计算为确定性公式 + LLM 综合解读，无外部 API 依赖。 不适用于：八字命理、紫微斗数、塔罗占卜、奇门遁甲等其他领域 → 建议使用 fortune-hub 路由。

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