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LLM-based extraction of per-paper workflow graphs and content/structural reproducibility scoring.
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Based on SOC occupation classification
Skill for the core infrastructure module providing logging, configuration, exception handling, progress tracking, checkpoints, retry logic, pipeline execution, performance monitoring, security, file operations, and multi-project orchestration. Use when setting up logging, loading config, handling errors, running pipelines, or monitoring performance.
Skill for the documentation infrastructure module providing figure management, image handling, markdown integration, and API glossary generation. Use when managing research figures, inserting images into manuscripts, auto-numbering figures, or generating API documentation.
Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or managing Ollama models.
Python CLI and menu orchestration for pipeline runs — project slug validation, interactive picker, PipelineRunner wrapping PipelineExecutor, per-stage logs, secure pipeline wrapper. Use when extending run.sh behavior, fixing menu/CLI routing, or testing discovery helpers.
Skill for the project management infrastructure module providing multi-project discovery, structure validation, and metadata extraction. Use when discovering active projects, validating project directory structure, or extracting project configuration metadata.
Skill for the publishing infrastructure module providing academic publishing workflows including BibTeX CLI citation generation, APA/MLA citation helper functions, DOI management, Zenodo publication, arXiv submission preparation, GitHub releases, PyPI and TestPyPI package distribution, static-site deployment to GitHub Pages / Cloudflare Pages / Netlify, multi-target long-horizon archival (IPFS Pinata, Web3.Storage, Software Heritage), a central platform registry, and publication readiness validation. Use when publishing research, generating citations, minting DOIs, distributing Python packages, deploying documentation sites, or preparing archival submissions.
| name | Reproducibility Workflow-Graph Assessment |
| description | LLM-based extraction of per-paper workflow graphs and content/structural reproducibility scoring. |
You are interacting with the src/reproducibility/ module. This decomposes a paper's own described pipeline (source/method/experiment/sink steps) into a workflow graph and scores how reproducible that pipeline is from the paper's own text.
ContentWeights, StructuralWeights) within Python scripts. Bind them to LLMConfig and the reproducibility_assessment block of config.yaml, loaded via config_loader.load_reproducibility_config().WorkflowNode must carry a non-empty source_quote copied verbatim from the paper's full text. Never accept or fabricate a node without one — this is the node's entire evidentiary basis.project_config.fulltext.enabled is false and no --fulltext-dir override is supplied, treat a reproducibility_assessment run as a no-op that still yields valid, empty-but-well-formed outputs — never mistake the warning for a crash.