AI Agent2026
Provides a curated collection of DESIGN.md files extracted from real websites so AI coding and design agents can generate visually consistent UIs from a single markdown file. Includes previews, extracted tokens, and ready prompts for quick agent integration.
AI Agent2026
Provides reusable 'Agent Skills'—modular skill packages for integrating Gemini, Managed Agents, and Google Cloud services into installable agent components. Focuses on ready-made connectors and recipes for common Google product workflows, speeding up building and extending agents on Google's Agent Platform.
AI Agent2026
Runs an LLM-driven agent loop that iteratively proposes, applies, tests, and commits small repo changes—each successful iteration becomes a separate git commit while failures are rolled back or preserved for repair. Supports multiple agent backends, worktrees for concurrency, live terminal status, and optional per-iteration pushes.
AI Agent2026
Turns natural-language instructions into runnable trading research: data loaders, strategy generation, backtests, reports, and optional broker connectors. Focuses on a tool-driven agent model (36+ MCP tools, 77 finance skills) and an Alpha Zoo of 452 pre-built alphas for reproducible research and gated agentic trading.
AI Agent2026
Provides a lightweight Python harness that turns LLMs into working agents with tool-use, skills, persistent memory, permission controls and multi-agent coordination. Ships with a CLI/React TUI, 43+ built-in tools, a plugin/skill system and the ohmo personal-agent for chat gateways. Best for developers prototyping agent workflows and multi-agent experiments.
AI Coding2026
Turns a repo's code, docs, PDFs, images, and videos into a queryable multimodal knowledge graph for AI coding assistants. Uses deterministic AST extraction for code and LLM-based semantic extraction for other assets, exporting interactive HTML, JSON, and a human-readable audit report.
AI Model2026
Generates and iterates on long‑horizon agentic plans and code — designed to stay productive across many rounds of tool calls and experiments. Emphasizes iterative reasoning, stronger repo/terminal automation and code generation than GLM‑5, and can be served locally for research and autonomous-agent workloads.
AI Agent2026
Provides a cloud-backed shared memory and skill-propagation layer for coding agents: captures session traces, mines recurring patterns into reusable SKILL.md, and shares capabilities across agents in real time. Features hybrid semantic+lexical search, BYOC storage, and a VFS for traces — built for team workflows and agent orchestration.
AI Client2026
Compresses LLM/agent replies into a terse “caveman” style to cut output tokens (~65–75%) while preserving technical accuracy. Offers per-agent skills, intensity modes, memory-compression and middleware to lower token cost and extend usable context.
AI Agent2026
Automates scanning and evaluating job listings with LLM-driven agents, then generates ATS-optimized, per-role PDFs and a unified tracker. Supports batch processing and terminal-first workflows with structured A–F scoring and portal scanners.
AI Agent2026
Acts as a local git proxy that runs an AI-driven validation pipeline in a disposable worktree, only forwarding the branch and opening a PR after every check passes. Runs review, tests, docs, and lint in isolation, applies safe auto-fixes, supports multiple agent providers, and pauses for human approval when intent would change.
AI Agent2026
Provides a brain layer for AI agents that synthesizes answers, traverses a self-wiring knowledge graph, and highlights gaps in team knowledge. Ships hybrid retrieval, citation-aware synthesis, and MCP integrations for Claude/Codex to power meeting prep and company-wide memory.