Results for “style”

Generate a single continuous cinematic shot video with no cuts, using text or image prompts and configurable style, duration, and aspect ratio.

Define every visual attribute as structured VGL JSON for deterministic, reproducible image generation with Bria FIBO models, covering objects, lighting, camera settings, composition, and style.

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Generate images using Pruna's optimized P-Image models via the inference.sh CLI, supporting text-to-image, LoRA styles, image editing, and multi-image compositing.

Generate AI-powered podcast-style audio narratives from text using Azure OpenAI's GPT Realtime Mini model via WebSocket, with full-stack implementation from React frontend to Python FastAPI backend.

Learn best practices for writing effective AI video generation prompts, covering shot types, camera movements, lighting, style keywords, and model-specific tips for Veo, Seedance, Wan, Grok, and others.

Mantém a coerência visual entre peças geradas por IA usando modelo fixo, prompt base, seed e referência de estilo, com teste de coerência e biblioteca de prompts.

Produces a complete narrated, subtitled, scored explainer video from a single topic prompt using a six-stage pipeline with script, voiceover, keyframes, animation, music, and local assembly.

Expert voxel artist with deep knowledge of MagicaVoxel workflows, palette design, animation techniques, mesh conversion, and game engine optimization. This skill represents insights from professional voxel artists, Teardown developers, and the MagicaVoxel community - knowledge Claude wouldn't normally have about this specialized art form. Use when "voxel, voxel art, magicavoxel, qubicle, voxel model, voxel animation, voxel game, blocky art, minecraft style, teardown style, voxel character, voxel environment, voxel optimization, voxel export, voxel to mesh, marching cubes, greedy meshing, voxel palette, destructible voxels, voxel, 3d, pixel-art, game-dev, magicavoxel, qubicle, animation, optimization, indie-games, retro, blocky" mentioned.

Guides contributors through adding, updating, publishing, documenting, validating, and wiring public skills in the BuilderIO/skills repository, covering plain skills, instruction-style skills, app-backed/MCP skills, and Plan skill sync.

Generates AGENTS.md files for repository folders to provide coding agents with project-specific context including build commands, testing instructions, code style, project structure, and operational boundaries, only where AGENTS.md is missing.

Builds detailed voice profiles for characters, narrators, brands, or AI agents, covering diction, syntax, rhythm, worldview, signature patterns, and failure modes, and can reverse-engineer profiles from writing samples or audit existing ones.

Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

Generates a vertical 9:16 UGC-style video ad from a person photo, product photo, and optional script by creating a lifestyle hero image and animating it with synced audio.

Reuse a running Chrome session for browser automation via Playwriter CLI + MCP. Use when the task depends on the browser the user already has open — existing logins, cookies, extensions, passkey-friendly flows, or live-tab continuity. Route repeatable fresh-browser or CI-style checks to `browser-harness` instead.

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Audit economic analysis outputs (fiscal briefings, macro briefings, market research, longlists, and other quantitative economic documents) against methodology standards, academic literature, and common errors. Runs structured checks across core categories including counterfactual, additionality, discounting, double counting, distributional analysis, Aqua Book RIGOUR, and Flyvbjerg-style strategic misrepresentation detection. Returns a RAG scorecard with issues ranked by severity.

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

Audits natural-language programming artifacts such as SKILL.md, AGENTS.md, CLAUDE.md, slash commands, plugin manifests, hooks, rules, and prompt files. Use when reviewing AI-agent repositories, checking manifest-vs-disk consistency, scoring skill or agent quality, adding NL artifact CI gates, or diagnosing vocabulary and version drift across Claude Code, Codex, Cursor, Gemini, and Antigravity-style projects.

Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.

Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.

Bidirectional conversion between Clojure and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Roc specific patterns. Use when migrating Clojure applications to Roc's platform model, translating dynamic functional code to static functional style, or refactoring REPL-driven code to compile-time verified patterns. Extends meta-convert-dev with Clojure-to-Roc specific patterns.

Build and maintain a structured personal-context portfolio for the project owner — identity, working style, goals, team, decision patterns. Includes both an interview protocol for bootstrapping and observable-signal patterns for incremental refinement. Use to bootstrap an owner profile (interview), to refine an existing profile (target one file), or to incrementally update the profile based on observed patterns from a normal session (the agent watches for signals and proposes additions when evidence accrues).

AI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).

Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.

Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.

Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.

Two-party working posture — user as director, agent as executor. Every fork, tradeoff, and taste choice is surfaced via batched AskUserQuestion with structural framing, a recommended default, and concrete previews when comparison is visual, so the human steers direction while the agent handles implementation. Eliminates the review-bottleneck (no giant diff to approve at the end — review is distributed across picks) and prevents codebase-understanding debt (the user remembers the architecture because they picked it). Use whenever the user invokes /duet, or says "work with me", "ask before", "check with me", "I want to decide", "don't assume", "human-in-the-loop", "co-author", "pair with me", "duet", or whenever a task clearly involves aesthetic, architectural, or irreversible strategic decisions — even without those exact words. Pair with the Duet output style to minimize cognitive load between picks.