Evaluates job postings and produces tailored CVs, cover letters, and interview prep using a Claude Code-driven agent workflow. Distinguishes itself with a drafter–reviewer loop, mandatory PDF compilation and ATS text-layer verification, plus extensible portal scrapers and LaTeX templates.
Provides a local-first desktop workspace that runs and coordinates AI agents across your files, browser, and third-party tools with a shared, editable memory. Offers built-in models or bring-your-own-keys, one-click OAuth to 100+ integrations, and browser-driven agent automation.
Turns a domain description into a Claude Code agent team and the skills they use — auto-generates agent definitions and skill files from six pre-defined team-architecture patterns. Best for teams building structured multi-agent workflows on Claude Code.
Bridges an LLM assistant to your locally running TradingView Desktop via Chrome DevTools Protocol for AI-assisted chart analysis, Pine Script development, and UI automation. Key features: Claude Code MCP integration, a JSON/CLI 'tv' toolset, local-only operation and streamable chart data.
Integrates Codex into Claude Code so you can run read-only code reviews, steerable adversarial reviews, and delegate long-running tasks to a local Codex instance via slash commands. Uses the local Codex CLI/app server and Node.js; designed for developers who want seamless handoff between Claude Code and Codex.
Turns heterogeneous traces (chats, docs, emails, transcripts) into versioned, inspectable agent 'Skills' that capture both Persona and Work behaviors; supports multi-source collection, incremental merges and corrections, and installation across multiple agent hosts.
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.
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.
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.
Lets AI coding agents compile your documents and chat histories into a maintained Obsidian vault: it ingests sources, distills them into interconnected markdown pages, tracks deltas and provenance, and exposes query/lint/export skills across many agents.
Turns source documents into a linked, source-cited Obsidian wiki for grounded Q&A and research. Local-first vault workflows with content-addressed capture, provenance ledgers, BM25 retrieval, and Claude Code / Agent Skills integration.
An AI-agent value-investing research framework for Claude Code/Codex that encodes Buffett/Munger/Duan Yongping/Lilu methodologies into multi-agent skills — enforces decisive buy/sell outputs, multi-source financial rigor, and reproducible research workflows for investment decision-making.