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Preflight-check and grade research repositories for reproducible artifact evaluation.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "Research Repo Doctor" MCP server from askskill: Run: claude mcp add 'io-github-tom409114-rrdoctor' -- uvx rrdoctor
Run an artifact evaluation preflight on this research repository and return the run-path grade, missing items, and fix suggestions: <repo path or link>
A preflight result with a run-path grade and a list of key reproducibility issues.
Inspect this research repository for run-flow blockers, focusing on reachability from environment setup to execution entry points, and rank them by severity: <repo path or link>
A severity-ranked issue list showing which steps block evaluation or reproduction.
Run deterministic artifact evaluation preflight checks on these research repositories and compare their run-path grades: <repo A>, <repo B>
Grades for each repository plus a comparison to see which is more evaluation-ready.
Researchers can run a preflight check and run-path grading before submitting companion code for a paper to catch issues that may hurt reproducibility and evaluation.
Developers or evaluators can use it to quickly judge whether a research repository has a viable execution path and identify major blockers.
Students reproducing lab or class projects can run a preflight check first to see whether the execution path is clear and where failures may occur.
It is a deterministic artifact evaluation preflight and run-path grading tool for research repositories, used to check readiness before formal evaluation.
Based on the name and description, the main target is a research code repository, so you typically provide a repository to inspect. For exact input details, see the source repository.
The provided material does not include installation steps, runtime requirements, or key requirements. See the source repository for details.
Provides repository evidence before dependency adoption, codebase review, comparison, or merging.
Identify impactful papers to replicate, reusable tools, and replication verdicts.
Safely inspect local codebases with indexing, search, outlines, and AI-ready context.
Expose repository decisions, pitfalls, and guardrails to AI coding agents.
Detect doc drift and verify claims for MCP tools deterministically.
Understand codebase health, docs, git analytics, and architecture decisions.