paper-litflow: chapter-organized literature extraction

Python command-line workflow documented to classify PDFs by manuscript section, extract quotations and export chapter-organized Word material. Its extract-v2 route describes page markers, local quote matching and resumable processing; a companion skill lets an AI assistant drive the workflow.

Workflow level n/a Source Repository-documented Outcome Not empirically evaluated here

Evidence & verification

Source level

Repository-documented
Grounded in a maintainer repository/README. Availability, stars and forks do not establish research benefit or correctness.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Primary README documentation only; capability descriptions not evaluated outcomes. README introduction, extract-v2, export and companion Skill sections support the workflow. Speed and cost claims are author anecdotes/estimates and excluded from the proposed outcomes; quote matching is not semantic validation.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Getting started

Untested editorial guidance: inspect requirements and example sections, configure an approved model endpoint, then try the documented screen/from-list/extract-v2/export route on a small licensed corpus and compare quotations against the PDFs.

Maintenance

Mutable main branch retrieved; no maintenance reliability audit.

License

README licence statement only; no legal audit. Verify applicable terms before reuse.

Workflow & human oversight

Resource-level automation unspecified. The researcher defines the task, controls access and checks sources and outputs.

Workflow level is not an evidence score; higher is not better evidence.

Approach

Limitations

README-only review; no commands, tests, extraction accuracy or performance claims reproduced. Long-text character limits and keyword filtering can omit relevant evidence; OCR can corrupt quotations. A Word export of excerpts is not a validated literature review or exhaustive search. Author timing and cost assertions are not adopted as evaluated outcomes.

Reproducibility

No installation, tests or independent outcome reproduction.

Adoption requirements

Python dependencies, chapter configuration and accessible PDFs are needed; a model endpoint/API is required for AI steps. Tesseract is optional for scanned pages. Confirm rights and privacy before transmitting documents, protect API keys, budget API costs and manually check omissions and context.

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