https://github.com/rlarson94170/tyler_med
Claude Code skill: turn clinical-journal PDFs into a token-efficient Markdown wiki + evidence table (study design, DOI, sample size, data source) for literature review.
https://github.com/rlarson94170/tyler_med
claude-code claude-skill evidence-synthesis literature-review medical-literature pdf-to-markdown pymupdf systematic-review
Last synced: 2 days ago
JSON representation
Claude Code skill: turn clinical-journal PDFs into a token-efficient Markdown wiki + evidence table (study design, DOI, sample size, data source) for literature review.
- Host: GitHub
- URL: https://github.com/rlarson94170/tyler_med
- Owner: rlarson94170
- License: other
- Created: 2026-07-05T19:44:16.000Z (14 days ago)
- Default Branch: main
- Last Pushed: 2026-07-09T01:24:28.000Z (10 days ago)
- Last Synced: 2026-07-09T03:12:07.699Z (10 days ago)
- Topics: claude-code, claude-skill, evidence-synthesis, literature-review, medical-literature, pdf-to-markdown, pymupdf, systematic-review
- Language: Python
- Size: 68.4 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# tyler_med — Medical Literature Wiki Skill
A [Claude Code](https://claude.com/claude-code) skill that converts a folder of
**medical / clinical journal PDFs** (JVS, JAMA, NEJM, Ann Surg, EJVES, Cochrane, …)
into a token-efficient, two-tier Markdown wiki for literature review — with
study-design classification, DOI extraction, an evidence-table export, and
duplicate detection.
It is a medical-tuned fork of the general-purpose **`tyler`** skill from the
[`econtools`](https://github.com/johanfourieza/econtools) project (see
[Acknowledgements](#acknowledgements)).
## What it produces
```
WIKI_DIR/
├── index.md # papers grouped by study design (evidence hierarchy),
│ # with DOI links, sample size (N), and data source
├── index.csv # evidence-table skeleton (one row per paper)
├── index.json # same, machine-readable
├── papers/*.md # cleaned full text + rich YAML frontmatter
├── references/*.md # trimmed reference lists (kept, not discarded)
└── .wiki_state.json # incremental cache
```
Read `index.md` (or the CSV) to navigate 100+ papers cheaply; open an individual
`papers/*.md` only when you need the full text. The folder is also a ready-to-use
**Obsidian vault** (frontmatter → note properties, `[[wikilinks]]`, nested tags).
## Requirements
- Python 3.9+
- [`pymupdf4llm`](https://pypi.org/project/pymupdf4llm/) (bundles PyMuPDF / `fitz`,
used for embedded-metadata and DOI extraction)
```bash
pip install pymupdf4llm
```
## Usage
**As a Claude Code skill:** copy this folder to `~/.claude/skills/tyler_med/` and
invoke `/tyler_med`.
**Directly:**
```bash
python3 convert.py "PDF_DIR" "WIKI_DIR" [OPTIONS]
```
| Flag | Effect |
|------|--------|
| `-r`, `--recursive` | Scan `PDF_DIR` subdirectories |
| `--prefer-pdf-title` | Trust the PDF's embedded metadata title over the filename (good for older, badly-named files) |
| `--keep-references` | Keep references inline in each paper file |
| `--drop-references` | Discard references entirely (default: save to `references/`) |
| `--force` | Re-convert everything, ignoring the incremental cache |
## Medical-tuning highlights
- **Reliable titles** from embedded PDF metadata + DOI, not just the filename.
- **Mojibake / ligature repair** (e.g. `n ¼ 4,894` → `n = 4,894`, dropped `fi`/`fl`).
- **Study-design classification** (RCT, systematic review, meta-analysis, cohort,
case-control, guideline/consensus, scoping/narrative review, QI, protocol,
editorial). A named registry in the title (NSQIP, VQI, Medicare, …) implies a
cohort; an explicit `EDITORIAL` tag in a title is honoured.
- **Structured metadata:** DOI, sample size (`n=`), data source, trial/PROSPERO
registration IDs, and clean controlled tags.
- **Duplicate detection** (shared DOI or year+title) with `⚠️DUP-n` flags.
> **Note:** study design, sample size, and data source are heuristic (best-effort
> from the abstract). Verify anything load-bearing against the full text.
## Using it as an Obsidian vault
Open `WIKI_DIR` in Obsidian (or drop it into an existing vault). Each `papers/*.md`
carries YAML frontmatter that becomes note **properties**, and the index links every
paper with `[[wikilinks]]`.
**Tags are deliberately minimal and controlled** — only three nested namespaces, so
the tag pane and graph stay clean and queryable:
- `design/…` — study design (e.g. `design/cohort-study`, `design/randomized-controlled-trial`)
- `source/…` — data source (e.g. `source/nsqip`, `source/vqi`, `source/medicare/cms`)
- `year/…` — publication year (e.g. `year/2023`)
Per-keyword hashtags are **off by default** (they flood the graph); the human-readable
`keywords:` property is kept regardless. Pass `--keyword-tags` if you want them.
**Dataview** turns the frontmatter into a live evidence table. Examples:
````md
```dataview
TABLE study_type AS Design, year AS Year, sample_size AS N, journal
FROM "papers"
WHERE contains(data_source, "VQI")
SORT year DESC
```
````
````md
```dataview
TABLE WITHOUT ID link(file.link, title) AS Paper, doi
FROM #design/randomized-controlled-trial
SORT year DESC
```
````
For a **by-topic** view (what to cite for a given point), ask Claude to build
`index_by_theme.md` (see Step 4 in `SKILL.md`) — a curated thematic grouping with a
design badge and one-line contribution note per paper.
## Acknowledgements
This project is a derivative work of the **`tyler`** skill in the
[`econtools`](https://github.com/johanfourieza/econtools) project by
[@johanfourieza](https://github.com/johanfourieza), used and continued under the
MIT License. The original `tyler` was built for economics / social-science papers;
`tyler_med` re-tools it for clinical literature (study-design classification,
DOI/registry metadata, evidence-table export, mojibake repair, duplicate
detection). With thanks to the original author.
## License
Released under the **MIT License** — see [`LICENSE`](LICENSE). Copyright is held by
`johanfourieza` for the upstream `tyler` skill and by Robert A. Larson, MD for
the medical adaptation.