Conduct AI Research End to End With Skills

Conducts autonomous AI research from literature survey through experiment execution to paper writing by orchestrating 98 specialized skills across model architecture, training, evaluation, interpretability, and publication. Eliminates the burden of mastering dozens of fragmented tools by routing research tasks to domain-specific skills that handle implementation details, troubleshooting, and production workflows.

Once installed, load the autoresearch orchestrator, which manages the full research lifecycle using a two-loop architecture. The orchestrator accepts a research question, runs literature surveys and ideation through domain skills (model training, evaluation, mechanistic interpretability, etc.), executes experiments using framework-specific skills, and synthesizes results into papers and research presentations. Output: end-to-end research with context carried between stages.

Agents spend time testing hypotheses and iterating on ideas instead of debugging infrastructure, accelerating the pace of scientific discovery.

Skills are installed via npm package (@orchestra-research/ai-research-skills) to ~/.orchestra/skills/ with auto-detection of your coding agent (Claude Code, OpenCode, Cursor, Codex, Hermes, Qwen Code, Gemini CLI). Installation falls back to file copy on Windows if symlinks unavailable. Requires Node.js for npm-based installation; individual skills support Python 3.8+, PyTorch 2.0+, and CUDA 11.8+ depending on the framework.

Requires understanding which skill to invoke for your specific task (mitigated by autoresearch orchestrator routing). Some domains like distributed training require infrastructure knowledge. Individual skills may have incompatibilities with certain framework versions.