Dr. Claw: An AI Scientist Workspace for Vibe Research

Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-toend research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely preserved. We present Dr. Claw, an opensource workspace that wraps existing codingagent executors in a controllable and auditable human-in-the-loop workflow rather than introducing another autonomous agent. Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution, turning planning, execution, and writing into one traceable, recoverable loop. We demonstrate Dr. Claw through an interactive three-view scenario and a failure-recovery walkthrough, and evaluate it against a bare command-line agent sharing the same backend executor, so the comparison contrasts the whole orchestration layer (task graph, state objects, and skill library) with the agent it wraps. Holding the executor fixed, Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail. Demo access: repository https: //github.com/OpenLAIR/dr-claw, released under AGPL-3.0 with GPL-3.0 upstream components.

Introduction. Large foundation models and agentic tools have improved the five core AI research operations— literature review, idea generation, code implementation, results analysis, and drafting (OpenAI, 2023; Brown et al., 2020; Yao et al., 2022; Schick et al., 2023; Lu et al., 2024; Yu et al., 2025; Yang and Weng, 2025; Schmidgall et al., 2025; Baek et al., 2025). Command-line coding agents such as Claude Code and Gemini CLI push this further, living in the terminal, reading and writing project files, and sustaining context across long sessions (Chen et al., 2021; Barke et al., 2022; Dakhel et al., 2022; Jimenez et al., 2024; Yang et al., 2024). Yet these agents optimize execution, not control: the plan, intermediate decisions, and artifacts that make a research process reviewable are scattered or lost, and the human has few explicit takeover points. The bottleneck is now full-process orchestration rather than isolated capability: researchers still switch across tools for decomposition, scheduling, tracking, validation, and writing, which weakens reproducibility and delivery reliability.

Discussion / Conclusion. We presented Dr. Claw, an integrated system for end-to-end AI research that unifies state-object management and skill-based execution in one workspace to reduce cross-tool orchestration costs and improve workflow continuity. Evaluated with the same backend executor run inside versus outside Dr. Claw, which compares the whole orchestration layer against the agent it wraps rather than ablating its parts, the layer preserves the measured completeness of the output (a count of which research components are present, not a correctness check) while producing a more auditable, better-structured artifact trail, as shown through a persisted-process-model analysis and a non-destructive failure-recovery walkthrough; a retrospective human study over three stages provides complementary evidence on time, output quality, and integrated experience.