Search: "vision"

Automate Google Cloud Vision tasks via Rube MCP (Composio). Always search tools first for current schemas.

Detects timing side-channel vulnerabilities in cryptographic code. Use when implementing or reviewing crypto code, encountering division on secrets, secret-dependent branches, or constant-time programming questions in C, C++, Go, Rust, Swift, Java, Kotlin, C#, PHP, JavaScript, TypeScript, Python, or Ruby.

Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.

Prepares evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Supports authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.

Driving all platforms UI automation with vision-based model

Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).

Prove a deploy landed — the served revision is the commit you meant, its env is present, and the product does its job — instead of trusting the deploy command's exit code. Also catches server drift before a pull or deploy. Use after any deploy, rollout, restart or server-side fix ("задеплой", "выкати", "проверь на проде", "почему не обновилось"). Loaded by devops, l3-support, infra-provisioner.

Standardized cost-estimation framework for great_cto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.

A unified evaluation toolkit and leaderboard for rigorously assessing the scientific intelligence of large language and vision–language models across the full research workflow.

Open-source multi-provider AI coding agent: terminal TUI, Electron desktop cockpit (Cowork), and a 24/7 autonomous multi-AI fleet that runs free-first on local Ollama. 15 LLM providers (Claude, GPT, Gemini, Grok), ~110 tools, voice + vision companion, MCP.

sprezzature is a set of Claude / OpenCode skills with a curated design system for a front-end stack. It covers colors, accessibility, the user interface (UI), user experience (UX), audio, vision, and the path from the command-line interface (CLI) to the graphical user interface (GUI).