Research Repo Doctor

Get your research artifact ready for Artifact Evaluation before the deadline: scan the repo, scaffold the easy fixes, verify the run path, and generate the appendix.

Web demo for public repos: https://research-repo-doctor-bckncrcwwmg6jrbsrd6btj.streamlit.app/ If the hosted demo is waking up or reinstalling dependencies, the local zero-install command below is the reliable fallback:

uvx rrdoctor scan .

Maintainers can check anonymous demo access with python scripts/check_live_demo.py https://research-repo-doctor-bckncrcwwmg6jrbsrd6btj.streamlit.app/.

rrdoctor is a local CLI and GitHub Action for research artifact preparation. It audits whether a repo is reviewable, citable, and close to runnable; scaffolds safe mechanical fixes; maps findings to an AE-style readiness level; and turns the rest into a checklist any coding agent or human can finish.

Make it a pull-request gate

The GitHub Action is the main adoption path: run the CLI while preparing the artifact, then keep the same deterministic preflight on every pull request.

name: Reproducibility preflight

on:
  pull_request:
  push:
    branches: [main]

permissions:
  contents: read

jobs:
  rrdoctor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7
      - uses: Tom409114/research-repo-doctor@v0.2.24
        with:
          profile: standard
          fail-on: error

The full GitHub Action guide covers sticky PR comments, new-finding baselines, agent plans, appendices, and complete AE prep packets.

AE deadline loop

uvx rrdoctor prepare . --profile acm --out-dir rrdoctor-prep

# Or run the pieces explicitly:
uvx rrdoctor scan . --profile acm
uvx rrdoctor fix . --write
uvx rrdoctor appendix . --profile acm --output ARTIFACT_APPENDIX.md
uvx rrdoctor verify . --profile acm
uvx rrdoctor verify . --profile acm --run --timeout 600 --fail-on error  # trusted repos only
# Or pin the official quickstart command as the dynamic gate:
uvx rrdoctor verify . --profile acm --command "python train.py config/default.py" --run --timeout 600 --fail-on error

For trusted repositories, rrdoctor verify --run can go beyond static checks. For supported Python repositories it creates a temporary isolated environment, installs declared dependencies, and executes the declared entrypoint there under a timeout. Other ecosystems retain an explicit resolver preflight. With the default gate (--fail-on error), failed or blocked dynamic L2/L3 steps return a nonzero exit code. Use --command when the artifact has a specific smoke-test or quickstart command that reviewers should run. Dynamic mode may run dependency build/install hooks as well as the entrypoint, so never use it on untrusted code. rrdoctor prepare writes the report, agent plan, artifact appendix, and verification ladder into one local evidence directory.

Artifact Evaluation chairs and lab maintainers can use the AE chair guide for optional pre-submission wording and CI examples.

For public calibration evidence, see the 80-repository data brief and the nanoGPT first-run regression case study.

What it catches

• "Your --seed flag does nothing." RRD052 spots code that declares a seed option but never calls random.seed, np.random.seed, torch.manual_seed, tf.random.set_seed, or random_state=seed.
• "This worked on my laptop." Local-only data paths, missing data provenance, and undocumented retrieval steps.
• "The environment silently changed." Unpinned dependencies, missing runtime versions, undeclared imports, and absent dependency manifests.
• "The notebook lies." Stale outputs, out-of-order execution, checkpoint artifacts, and secret-like notebook output.
• "Reviewers cannot tell how to cite or rerun this." Missing license, citation, CI, tests, changelog, results provenance, or experiment entrypoint.

Install

Run once, without installing:

uvx rrdoctor scan .

Alternatives:

pipx run rrdoctor scan .
pip install rrdoctor
rrdoctor scan .

Developer install from source:

git clone https://github.com/Tom409114/research-repo-doctor.git
cd research-repo-doctor
python -m pip install -e ".[dev]"
rrdoctor scan .

Fix the easy gaps

Let rrdoctor create the safe scaffolding for you. It is deterministic, idempotent, and never overwrites existing files.

rrdoctor fix . --write

It can scaffold missing governance docs, citation metadata, data/results provenance notes, a reproducible-seed helper, changelog entries, and common research .gitignore entries. The hard parts become a reviewable plan:

rrdoctor plan . --output plan.md

Use with your coding agent

Paste this into Claude Code, Cursor, GitHub Copilot, or any other coding agent:

Use rrdoctor as the deterministic, offline, no-API-key grader for this research repo.

Run:
rrdoctor scan . --format json --output baseline.json
rrdoctor plan . --output plan.md

Work through plan.md without weakening rrdoctor checks.

Definition of done:
rrdoctor scan . --baseline baseline.json --fail-on-new error

The final command is the objective gate: it verifies the agent's work against the starting baseline and fails only on newly introduced errors.

Copyable agent templates are available for Agent Skills / Claude Code-style workflows and Cursor project rules under integrations/.

Keywords: research software, reproducibility, artifact evaluation, repository audit, auto-fix, coding agents, AGENTS.md, GitHub Action, notebooks, data availability, citation metadata.

Help calibrate the rules

The fastest way to improve rrdoctor is real scan feedback from real research repositories. After one scan, the low-friction 10-minute trial report asks only what was useful, wrong or missing, and whether the tool fits a real deadline. For a focused rule report, please open a false-positive, false-negative, scan-case, or new-rule issue. Include the rule ID, command, rrdoctor version, and a sanitized minimal repo shape. See feedback and calibration for the short checklist.

Why this matters

Research code often lands on GitHub under deadline pressure. A reviewer or future lab member finds a promising repository and then loses hours because the environment is underspecified, data paths are local, notebooks contain stale outputs, dependencies are unpinned, or the citation is unclear.

Research Repo Doctor turns those recurring release blockers into deterministic checks with concrete remediation - and, where it is safe to do so, scaffolds the mechanical starting points. It is built to sit in the ordinary maintenance path: run locally while preparing a release, then run automatically on pull requests through GitHub Actions.

The audit runs without an AI API key, network access, or hosted service. That same determinism makes it an honest grader: it can verify fixes made by a person or a coding agent.

audit -> fix -> plan -> (your coding agent / you) -> verify -> PR
  |       |       |                                  |
  |       |       rrdoctor plan                      rrdoctor scan --baseline
  |       rrdoctor fix --write                       --fail-on-new error
  rrdoctor scan

What's new in 0.2.24

• Better systems-artifact coverage: existing checks now recognize Cargo, CMake, Meson, container, and Nix evidence plus documented artifact scripts and Cargo commands across entrypoint, test, CI, and release workflows.
• Quieter static scans: target-source SyntaxWarning noise is suppressed, and escaped Python exception text no longer looks like a local Windows path.
• A rechecked first-run trust case: public PyPI rrdoctor==0.2.23 still scores nanoGPT as Functional at 76/100 with 0 errors, while the original noisy RRD050 and RRD063 findings remain absent.
• An explicit JOSS boundary: the draft-paper notes now separate the more-than-six-month public-history gate from research-use, authorship, conflict-of-interest, and AI-disclosure evidence that maintainers must supply.
• A configuration file that actually governs the scan: CLI and MCP calls load the target repository's .rrdoctor.yml from any working directory; profile, report, threshold, failure, include/exclude, and rule settings now work beneath explicit CLI overrides.

What's new in 0.2.23

• A safer release path: the hosted demo can stay pinned to the latest published PyPI package while a new release propagates, avoiding broken Streamlit deployments during Trusted Publishing.
• A first-class GitHub Action path: a compact pull-request workflow now appears near the top of the README and is guarded by public-readiness tests.
• A shorter feedback loop: completed web-demo scans link to a structured 10-minute trial report for useful findings, false positives, false negatives, and real deadline fit.
• Official MCP Registry distribution: v0.2.24 is published as io.github.Tom409114/rrdoctor, with MCP support remaining an optional extra rather than a core scanner dependency. Registry inclusion is distribution metadata, not endorsement or evidence of adoption.
• Stronger dynamic verification coverage: an offline integration test proves L3 can install and import a declared local wheel inside its temporary Python environment.

What's new in 0.2.22

• Real Python environment verification: rrdoctor verify --run now creates a temporary isolated venv, installs the repository's declared dependencies, and runs L3 with that interpreter and PATH instead of stopping at a resolver dry run.
• Honest execution boundaries: verification reports disclose dependency build-hook risk, redact machine-specific temporary paths, and remove the temporary environment after the run. Other ecosystems retain an explicit resolver preflight.
• Stronger isolation checks: inherited PYTHONHOME and PYTHONPATH values are removed, and versioned launchers such as python3.10 are redirected to the temporary environment.

What's new in 0.2.21

• More honest corpus evidence: the public 80-repository calibration corpus now has 80 focused review notes, 0 repositories awaiting focused review, and a concise data brief that explains what the corpus does and does not prove.
• Lower-noise model-release scans: RRD050 and verify now recognize common model-release entrypoints such as demo.py, inference.py, predict.py, sample.py, and generate.py in root, scripts/, and tools/ layouts.
• Less noisy secret checks: RRD063 and RRD090 now require stronger entropy evidence for generic api_key/token/secret/password assignments while still flagging provider-shaped keys.
• More agent integrations: the repository includes copyable GitHub Copilot instructions alongside the existing Agent Skill and Cursor templates for the baseline -> plan -> verify loop.
• Clearer launch artifacts: the JOSS draft now states the evaluation evidence and limitations more explicitly, and the live-demo check now follows Streamlit's anonymous redirect handshake before deciding whether the app is reachable.

What's new in 0.2.20

• Lower-noise mature scientific package scans: RRD010 now recognizes common license filenames such as LICENSE.txt, and RRD043 ignores CI/devcontainer paths, tests/fixtures, URL path segments, and documented placeholder/example absolute paths.
• Lower-noise library and secret heuristics: RRD050 no longer treats mature package/library projects, including common nested package/ layouts, as missing paper experiment entrypoints, and RRD090 ignores URL query tokens, local function-call or method-call token variables, generic fake tokens in test helpers, and provider-looking substrings embedded inside longer biological/test sequences.
• More reviewed corpus evidence: SciPy is a focused review case and an expected-absent regression gate for RRD010 and RRD043; scikit-image, JAX, NetworkX, Keras, Transformers, PyTorch Lightning, Biopython, torchvision, MDAnalysis, QuTiP, ESM, stable-diffusion, detectron2, DINO, StyleGAN2-ADA PyTorch, instant-ngp, Big Vision, latent-diffusion, taming-transformers, generative-models, pytorch-image-models, Brax, ArviZ, PyMC, Pyro, TensorFlow Probability, statsmodels, Optax, and the remaining corpus entries now add first-run trust gates or focused review evidence. The latest 80-repository corpus gate has 0 expected-absent regressions, 80 reviewed notes, and 0 repositories still awaiting focused review.
• Less template-like auto-fix output: rrdoctor fix --write can now read simple literal setup.py metadata statically, without executing repository code, when generating citation and provenance scaffolds.
• More filled Artifact Appendix access notes: rrdoctor appendix reuses the same local metadata inference to pre-fill repository URLs and versions for legacy setup.py/setup.cfg projects.
• More realistic L2 environment checks: rrdoctor verify --run now resolves common nested Python requirement files such as requirements/base.txt and requirements/main.txt, plus .yaml Conda environment files, instead of skipping those repository layouts.
• Lower-noise notebook secret checks: RRD063 now shares the test/fixture generic fake-token suppression used by RRD090, while still flagging standalone provider-style keys.

What's new in 0.2.19

• Lower-noise experiment entrypoint detection: RRD050 now recognizes package-level research binaries such as t5x/train.py, documented python3 ${T5X_DIR}/t5x/train.py commands, and notebook-first artifacts with clearly named demo/example/reproduce notebooks such as graphcast_demo.ipynb.
• More first-run corpus evidence: focused review notes now cover 32/60 seed corpus repositories. The latest 60-repository static corpus scan has 0 expected-absent regressions and keeps t5x and GraphCast as entrypoint regression gates.

What's new in 0.2.18

• Lower-noise dependency checks: RRD034 now parses Python AST imports instead of regex-matching source text, so comments, docstrings, and prose examples do not look like missing packages.
• Runtime-focused dependency signal: docs, tests, benchmarks, vendored code, maintainer tooling, conftest.py, build-system requirements, and local sibling modules are filtered out before dependency-gap reporting.
• More corpus review evidence: focused review notes now cover 30/60 seed corpus repositories, including scikit-learn, Astropy, scvi-tools, and DINOv2 checks for dependency-signal noise.
• Current install path: PyPI, GitHub Action examples, demo requirements, citation metadata, and the self-scan report are aligned to this release.

What's new in 0.2.15

• Clearer verification evidence: rrdoctor verify reports now lead with the gate outcome, failure threshold, timeout, trust boundary, rerun command, and the source of any L3 dynamic command.
• Stronger Artifact Appendix scaffolding: rrdoctor appendix now pre-fills local README/project metadata, dependency manifests, data/results docs, config files, and detected entrypoint commands where available.
• More useful generated data notes: rrdoctor fix --write carries over candidate dataset URLs, DOIs, README data commands, and local data scripts when scaffolding DATA.md.
• Maintainer launch gates: python scripts/check.py and python scripts/check_public_readiness.py provide cross-platform local checks for release, JOSS, Artifact Evaluation, and public outreach readiness.

What's new in 0.2.14

• One-command AE evidence packet: rrdoctor prepare writes the static report, agent fix plan, Artifact Appendix, and verification ladder into one local directory for deadline handoff.
• CI-uploaded AE packet: the GitHub Action now supports prepare: "true" and prepare-output, so pull requests and release gates can upload the same reviewer-ready packet.
• Pinned run-path verification: verify --command "...", the Action verify-command input, and the MCP verify tool let maintainers pin the official quickstart command and timeout as the L3 gate.
• Lower first-run noise: MAE-style root main_*.py scripts, AlphaFold-style random_seed= plumbing, test-file randomness, and placeholder absolute paths are handled more conservatively.

What's new in 0.2.13

• Scan reports now lead to the AE workflow: Markdown reports and agent fix plans now include next-step commands for rrdoctor plan, rrdoctor appendix, static rrdoctor verify, and trusted-only dynamic verify --run.
• Stronger auto-fix scaffolds: generated AGENTS.md files now include the scan -> plan -> baseline verification loop, and generated results-provenance notes include local repository context, current result files, and a structured result inventory table.
• Better adoption materials: public docs now include a feedback/calibration path and an Artifact Evaluation chair guide with optional pre-submission wording and CI examples.

What's new in 0.2.12

• Trusted dynamic Action gate: GitHub Action users can now set verify-run: "true" plus verify-fail-on: error so trusted dynamic verification blocks CI while still uploading the verification report.
• Agent distribution templates: repository Copilot instructions, an Agent Skill template, and a Cursor project rule make the deterministic scan -> plan -> verify loop copyable across coding-agent workflows.
• Tighter evidence wording: corpus and JOSS draft wording now distinguishes focused review notes from full manual repository audits.

What's new in 0.2.11

• First-run trust tuning: README install/run commands, seeded local RNGs, PyTorch parameter initialization, UUID-like identifiers, and classic ML repos now produce fewer false positives.
• Real dynamic gate: rrdoctor verify --run --fail-on error now exits nonzero when dependency resolution or the detected run path fails or is blocked.
• More calibration evidence: 22 focused review notes are now committed, including BERT, CLIP, improved-diffusion, MAE, and AlphaFold follow-ups with expected-absent checks for fixed noisy findings.

What's new in 0.2.10

• More reliable corpus calibration: the public evaluation-corpus runner now falls back to GitHub archives when git clone times out, keeping first-run trust checks less dependent on flaky transport.
• Cleaner maintainer automation: first-party workflows and documentation examples now use current Node 24-compatible GitHub Actions releases.
• Sharper diagnostics: rrdoctor doctor now reports optional MCP integration availability only when the package and import-time dependencies actually load.

What's new in 0.2.9

• Clearer first-run CLI behavior: rrdoctor --version now reports the installed package version, and running bare rrdoctor prints the root help page successfully.

What's new in 0.2.8

• Better README run-path recognition: README-documented python -m package.train ... commands now count as experiment entrypoints when they map to local repository modules.
• Stronger dynamic verification for ML launchers: rrdoctor verify now recognizes module-runner commands such as python -m torch.distributed.run train.py ... when they include a local Python entrypoint.

What's new in 0.2.7

• Better citation scaffolds: rrdoctor fix --write now reads structured PEP 621 and Poetry metadata, preserves multiple authors, normalizes SSH git remotes, and handles git worktree origin URLs when generating CITATION.cff.
• Lower-noise dependency checks: RRD034 now understands PEP 621 environment markers and Poetry dependency groups, reducing undeclared-import false positives.

What's new in 0.2.6

• Lower-noise secret checks: Rcpp Generator token markers and public pkgdown docsearch.api_key search configuration no longer trigger RRD090, while generic credential-like API keys still do.
• More reliable corpus scans: the evaluation runner now falls back to GitHub archive downloads when git clone transport is flaky, without installing or executing target repositories.
• More manual calibration evidence: the current public corpus snapshot covers 60/60 successful static scans, 22 focused review notes loaded in that snapshot, and 0 expected-absent regressions.

What's new in 0.2.5

• Model-release entrypoints: README-documented python scripts/*.py / python tools/*.py commands and pyproject-declared CLI commands now count as experiment entrypoints, reducing first-run false positives on repositories such as Segment Anything and Whisper.
• ML tools entrypoints: common tools/train.py, tools/test.py, and related ML framework commands now count for RRD050.
• Seed helper scaffolding: rrdoctor fix --write can scaffold a reproducible set_global_seed(seed) helper for RRD052 without overwriting project code.
• Corpus regression gates: entrypoint fixes are backed by focused review notes and expected_absent checks in the public evaluation corpus.

What's new in 0.2.4

• First-run trust improvements: root-level train.py/main.py/run.py, Snakemake/Nextflow workflows, and README run commands count as experiment entrypoints.
• Lower-noise security checks: notebook and repository secret detection now requires high-confidence credential-like values before raising blocking errors.
• More realistic README checks: concrete training, evaluation, benchmark, workflow, or reproduction commands count as evidence for reproducing results.
• Corpus-backed rule calibration: the public evaluation corpus tracks false-positive and false-negative review notes, expected-absent regression gates, and aggregate rule frequencies.
• Release hygiene: citation guidance detection recognizes README Citing sections, BibTeX, DOI links, and "please cite" text; local git tags count as deterministic version evidence.
• Release polish: the demo GIF is generated, issue access is open, and the committed self-scan report is 100/100.

What's new in 0.2.0

• rrdoctor fix provides deterministic, idempotent auto-fix for common gaps (governance docs, citation metadata, data/results provenance, seed helper scaffolding, changelog, ignore entries). Never overwrites.
• rrdoctor plan emits a tool-agnostic fix plan you can hand to any coding agent; every task names the deterministic check that verifies it.
• Baseline gating: rrdoctor scan --baseline report.json --fail-on-new error fails only on newly introduced findings, so large repos can adopt the audit incrementally.
• rrdoctor badge emits a Shields.io endpoint or SVG artifact-readiness badge.
• Artifact readiness labels map findings to an AE-style level: Available, Functional, or Reproduced-ready. The numeric score remains as a secondary triage signal.
• First-class PR automation: the Action posts a sticky PR comment, writes a job summary, and can attach the fix plan, using only the built-in GITHUB_TOKEN.
• New rules include unpinned dependencies, committed notebook checkpoints, pre-commit config, and an AGENTS.md task guide for agent and human contributors.

Quickstart

rrdoctor scan .                   # deterministic audit (Markdown report)
rrdoctor fix . --write            # apply safe scaffolding for easy gaps
rrdoctor plan . --output plan.md  # tool-agnostic work order for the rest
rrdoctor scan . --format json --output baseline.json --fail-on none
rrdoctor scan . --baseline baseline.json --fail-on-new error  # gate regressions

Stricter gate and report file:

rrdoctor scan . --profile strict --fail-on warning --output rrdoctor-report.md

Machine-readable and agent output:

rrdoctor scan . --format sarif --output rrdoctor.sarif --fail-on none
rrdoctor scan . --format agent --output fix-plan.md

Before a submission deadline:

rrdoctor prepare . --profile acm --out-dir rrdoctor-prep          # one local AE packet
rrdoctor appendix . --profile acm --output ARTIFACT_APPENDIX.md   # appendix + checklist mapping
rrdoctor verify . --profile neurips                               # L1/L2/L3 ladder (static)
rrdoctor verify . --run --timeout 600 --fail-on error              # build + run gate (trusted repos)
rrdoctor verify . --command "python train.py config/default.py" --run --timeout 600

Submission profiles: acm, neurips, icml, ml-paper, fair4rs, joss (alongside the general minimal/standard/strict/ml tiers). Static dependency and runtime checks also understand R, Julia, Rust/Cargo, CMake-based builds, containers, and Nix environments, not just Python and JavaScript.

The audit -> fix -> verify loop

A deterministic checker is reproducible and trustworthy but cannot write prose or judge intent. A coding agent edits well but needs a precise specification and an objective definition of done. Research Repo Doctor gives you both:

1. Audit: rrdoctor scan produces deterministic findings.
2. Fix the easy ones: rrdoctor fix --write scaffolds governance docs, citation metadata, provenance notes, a seed helper, a changelog, and ignore entries (idempotent, never overwriting).
3. Plan the rest: rrdoctor plan emits a tool-agnostic work order. Paste it into the coding agent of your choice, attach it to an issue, or work it by hand.
4. Verify: re-run the audit against a baseline. Because verification is deterministic and key-free, it works as an honest grader for changes from any source.

See docs/agent-workflows.md and docs/autofix.md.

GitHub Action

Add one workflow to many repositories and get consistent reproducibility reports on pull requests and pushes. The Action requires no API key.

name: Reproducibility audit

on:
  pull_request:

permissions:
  contents: read
  pull-requests: write

jobs:
  rrdoctor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7
      - uses: Tom409114/research-repo-doctor@v0.2.24
        with:
          profile: standard
          fail-on: none
          comment-pr: "true"     # sticky PR comment with the report
          step-summary: "true"   # report in the job summary
          plan: "true"           # attach an agent-ready fix plan
          appendix: "true"       # attach an Artifact Evaluation appendix
          verify: "true"         # attach the L1/L2/L3 verification ladder
          prepare: "true"        # upload a complete AE prep packet directory
          # For trusted repos, add verify-run: "true" and verify-fail-on: error

For new-finding gating and a committed baseline, see docs/pull-request-automation.md.

Example output

Research Repo Doctor Summary
Profile: standard
Readiness: Functional
Score: 64/100
Errors: 0
Warnings: 5
Rules evaluated: 32

How to fix first:
- RRD030 No dependency manifest found: Add pyproject.toml, requirements.txt, or another manifest.
- RRD040 Data availability documentation missing: Add DATA.md, docs/data.md, or a README section.

Worked examples live in examples/reports/, including a fix plan and a self-scan report.

Commands

Rule categories

Documentation, environment, data, experiments, notebooks, citation, governance, testing, CI, security, release, and metadata. The full table is in docs/checks.md; auto-fixable rules are marked there.

Reproducibility stance

Research Repo Doctor does not claim to prove a paper is reproducible. It checks release hygiene that makes reproduction possible to attempt. Reports are heuristic and should be reviewed by maintainers. Generated fixes are starting points and contain placeholders to complete before release.

Philosophy

Deterministic first. The scanner is understandable, testable, and useful with no network access. The core scanner will not add network calls, require a hosted-service API key, or fabricate adoption metrics. AI is something you bring to act on the output - never a dependency of the audit itself, and never tied to a single tool.

Configuration

version: 1
profile: standard
paths:
  exclude: [".git", ".venv", "node_modules", "__pycache__"]
thresholds:
  large_file_mb: 50
  large_notebook_output_kb: 1024
rules:
  RRD032:
    enabled: false
  RRD042:
    severity: warning
fail_on: error

See docs/configuration.md.

Contributing

Contributions are welcome. Start with CONTRIBUTING.md and AGENTS.md, open a rule request or false-positive report, and include a minimal fixture when possible.

Security

Do not report suspected credential exposure in a public issue. See SECURITY.md.

Citation

Use the included CITATION.cff or cite the version DOI for this release: 10.5281/zenodo.21289885. The stable concept DOI, 10.5281/zenodo.21045161, resolves to the latest archived release and preserves the complete version lineage.

A JOSS-style draft manuscript is available in paper/ for review. It is not a submitted manuscript and intentionally avoids unverified adoption claims; formal submission metadata will be updated only when it is true.

@software{research_repo_doctor_2026,
  title = {Research Repo Doctor},
  author = {{Research Repo Doctor Maintainers}},
  version = {0.2.24},
  year = {2026},
  doi = {10.5281/zenodo.21289885},
  url = {https://github.com/Tom409114/research-repo-doctor}
}

License

MIT. See LICENSE.