Automatically evolves Hermes Agent skills, prompts, tool descriptions and code using DSPy + GEPA — mutating text via API calls, evaluating trace-based failures, and selecting variants that pass tests and human PR review. No GPU training required; runs cost roughly $2–$10 per optimization.
A 23-skill Claude Code toolkit that composes an LLM-driven virtual engineering team (CEO, designer, eng manager, QA, security, release) into slash-command workflows — includes real-browser QA, a persistent GBrain memory, multi-agent integrations, and team auto-update semantics.
Reverse-engineers live websites into production-ready Next.js codebases: an AI-driven /clone-website skill extracts design tokens, assets, and exact component specs, then dispatches parallel builder agents to reconstruct pages. Recommends Claude Code (Opus 4.7) but supports many agents.
Curated marketplace of Claude Code plugins maintained by Anthropic, separating its own internally built plugins from vetted community submissions. Browse and install directly inside Claude Code via /plugin install {name}@claude-plugins-official.
Provides modular, AI-optimized SKILL.md instructions that ground LLMs in Android development workflows and can be installed into agent environments via the Android CLI; focuses on tasks where LLMs underperform.
Transforms articulated 3D asset creation into a programmatic, LLM-driven code-generation workflow that produces objects with semantic parts, robust geometry, and physical joints. Includes CLI generation, a local viewer, and pipelines for large-scale dataset contribution.
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.
A 228,557-example dataset of reasoning traces segmented into blocks with iterative, compressed "memento" summaries so LLMs can learn to manage long context. Includes a training-ready subset and a `full` subset with sentence/block-level annotations for research and SFT.
Provides a pytest-native framework to write safety and security tests for agentic AI applications. Defines adversarial attacks, benign-failure suites, and harm-category assertions with evaluation-driven checks and CI-friendly reporting, so red-teaming becomes testable and automatable.
Curated collection of resources, patterns, and reference implementations for building reliable AI agent harnesses—covering context delivery, tool/MCP design, memory, permissions, observability, verification, and orchestration for production agent engineering.
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.