Skill UI

This skill automates the research refinement process by entering an autonomous, multi-round review loop. It leverages any OpenAI-compatible LLM to score, critique, and guide necessary fixes until the work meets a high, expert-defined standard. It manages state persistence across rounds, simulating the rigorous process of professional peer review.

A foresight agent that runs a rigorous 12-step pipeline combining Claude’s qualitative intelligence with Python’s deterministic arithmetic to analyze high-stakes future scenarios, infer reasonable horizons, and surface quantitative signals for prediction requests.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

This sophisticated tool generates publication-quality AI illustrations and diagrams for academic papers. It implements a multi-stage, iterative workflow, using advanced LLMs (like Claude and Gemini) to parse user requests, optimize complex layouts, verify academic styling (e.g., CVPR/NeurIPS standards), and render high-fidelity figures. Ideal for researchers needing professional, complex visuals for their manuscripts.

Permanently deletes the context-mode knowledge base and resets session statistics. Users can choose between a project-wide wipe (deleting all indexed content and session data) or a targeted session wipe, ideal for isolating polluted data without losing project history. Note that this action is highly destructive and irreversible.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.

This skill enables the generation of high-quality, cinematic videos using a highly structured prompt format (JSON). Users define detailed cinematic elements—including background, characters, camera movements, dialogue, and audio—and can optionally provide a reference image to guide the visual style or content of the final output. It automates the complex AIGC video generation workflow.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

A powerful tool leveraging OpenRouter and the Gemini 3 Pro Image model to create, modify, and composite high-quality images. Supports three primary use cases: prompt-only generation from text, detailed single-image edits (inpainting/outpainting), and complex multi-image composition. Users can specify resolutions up to 4K, making it suitable for professional creative and design workflows.

This technique guides users on how to construct highly effective search queries for movies. When a movie question contains both general plot descriptions and highly specific details (such as exact quotes, precise rating language, or specific award names), the user must prioritize the most discriminative clue. By focusing on unique identifiers, this method significantly improves the accuracy and efficiency of information retrieval.

This technique focuses on extracting highly distinctive and unique personal details—such as daily habits, specific routines, or unique life experiences—from personal blog posts. Instead of relying on generic topic keywords, it leverages these unique details to construct precise, quoted search queries, significantly improving the accuracy of information retrieval across vast web corpora.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.