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From phd-deepread
Processes academic PDFs into structured Obsidian literature notes and 9-node critical-thinking canvases. Useful for researchers who want to deeply read, summarize, or critique papers and save results in Obsidian.
npx claudepluginhub heleninsights-dot/phd-deepread-workflowHow this skill is triggered — by the user, by Claude, or both
Slash command
/phd-deepread:phd-deepreadThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Turn a PDF into three artifacts the user can drop straight into Obsidian:
Builds Wikipedia-style Obsidian vaults from academic PDFs, extracting concepts into linked notes with atomic sentences and citations. Expands existing networks with new papers.
Reads, analyzes, and summarizes academic PDF papers — extracting metadata, research questions, methodology, results, and generating structured literature notes. Supports single paper deep dives, batch literature reviews, and cross-paper comparison.
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Turn a PDF into three artifacts the user can drop straight into Obsidian:
clauderules.md template.The user shares an academic PDF — typically by dragging it into the chat or pasting a path — and asks for any of:
If they only want a quick summary (no Obsidian artifacts), this skill is overkill — answer directly instead.
You drive the phd-deepread CLI on the user's behalf. The user does not type these commands.
phd-deepread run /path/to/paper.pdf
This produces three things in markdown_output/<paper_name>/:
<paper>.md — extracted textstructured_literature_notes/<paper>.md — this is a prompt, not a finished note<paper>.canvas — blank 9-node templateYou then write the literature note yourself. Read the prompt file, follow the instructions inside (it includes the full clauderules.md template), and overwrite the prompt file with the finished note. The prompt asks for YAML frontmatter, Dataview callouts, extensive wikilinks, and academic tone — follow that exactly.
After the note is written, populate the canvas from it:
phd-deepread canvas -o markdown_output/<paper>/<paper>.canvas \
--from-note structured_literature_notes/<paper>.md --overwrite
This maps note sections to canvas nodes by regex. Some nodes (assumptions, alternative-explanations) are auto-populated from related sections; the rest pull directly from the note. The user can refine any node in Obsidian.
phd-deepread extract /path/to/paper.pdf # PDF → markdown
phd-deepread generate markdown_output/<paper>/ -o notes/<paper>.md # build prompt
phd-deepread canvas -o notes/<paper>.canvas --title "..." --authors "..." --year "..."
phd-deepread batch /path/to/folder -o batch_output/ --create-canvases
Then loop over each <paper>_prompt.txt and write the finished note next to it.
phd-deepread verify markdown_output/<paper>/
Checks formatting, YAML frontmatter, callouts, wikilinks.
scripts/templates/clauderules.md — the literature-note template you must follow when writing the note. Loaded by generate.py via importlib.resources.scripts/templates/critical-thinking.canvas — base canvas layout used by canvas.py.Do not edit these as part of normal use.
phd-deepread doctor (if available) or fall back to python3 -m pip install --user phd-deepread-workflow and tell the user to open a new terminal.brew install tesseract (macOS) or sudo apt install tesseract-ocr (Linux).pip install --upgrade phd-deepread-workflow.examples/example-output.md — what a finished literature note looks like.examples/example-canvas.canvas — what a populated canvas looks like.Match those styles when writing your own.