Results for “natural-language-processing”

Process text with NLP. Use when tokenizing, analyzing sentiment, extracting entities, summarizing documents, or measuring similarity.

NLVR2: A Visual Reasoning Benchmark for Natural Language

Humanize text: strip AI-isms and add real voice.

当个人感到迷茫，不确定自己真正想要什么，或发现当前追求无法带来深层满足时，调用此模型来探索和澄清内在真实欲望

Rewrites text to remove AI writing patterns and add a natural, human voice, based on Wikipedia's guide to signs of AI writing.

Silently restructures natural-language prompts into the format best suited for the model currently executing the skill, then answers the rewritten version.

Humanize text: strip AI-isms and add real voice.

Language-specific super-code guidelines for elixir.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Builds NLP pipelines with Haystack for document search, QA, and LLM-powered applications.

Humanize text: strip AI-isms and add real voice.

Fast tokenization for NLP using Rust-based tokenizers supporting BPE, WordPiece, and Unigram algorithms, with training, alignment tracking, and padding/truncation.

Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

Prompt Optimizer

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Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file using trace_processor.

Rewrite text to remove AI writing patterns and add a natural, human voice.

Deduplicating Training Data Makes Language Models Better

LoRA: Low-Rank Adaptation of Large Language Models

Humanize LLM output so it reads like a careful human wrote it. Subtracts AI-isms (sycophancy, tricolons, em-dash overuse, "delve"/"tapestry"/"testament", hedging stacks, tidy five-paragraph shapes), engineers burstiness and calibrated uncertainty, and preserves technical accuracy. Supports intensity levels: subtle, balanced (default), full, voice-match, anti-detector. Use when user says "humanize this", "make this sound human", "de-slop this", "rewrite without AI tone", "match my voice", "less robotic", or invokes /unslop. Also auto-triggers when text-quality is requested.

去除或避免文本中的 AI 味。用于用户强制调用本 Skill，或写作/改写时提到“不要有 AI 味”“不要太假”“真情实感”“有人感”，或对已有文本/上一轮输出反馈“太像 AI”“太空泛”“太模板”“太官方”“不像我说的”等场景。

Extracts and formalizes domain terminology from a conversation into a consistent glossary saved to a local file.

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.

Conversational Pattern Restoration — Fix flat, robotic AI responses across any model and any personality. Restore YOUR natural conversational texture without triggering hype drift. Universal framework tested on 8+ models (Claude, GPT-4o, Grok, Gemini).

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Flamingo: A Visual Language Model for Few-Shot Learning

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

用自然语言描述生成目标音频。把一段场景描述（人声对话、环境声、音效、背景音乐等复合音频）一次性生成成音频。当用户描述一个声音场景、要求生成/合成/制作一段音频或声音、给出形如"角色：台词"的对话脚本要转成音频、或要按参考音频的音色说话时使用。支持两种模式：纯文本描述生成（T2A）和带参考音频生成（A2A，在描述中引用参考音频指定角色音色）

Large Language Model development, training, fine-tuning, and deployment best practices.

Humanize text: strip AI-isms and add real voice.

LLaVA-NeXT: Improved Reasoning, OCR, and World Knowledge