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Pdf Text Extractor

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WILLOSCAR
pdf-text-extractor

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namepdf-text-extractor
Stars
513
Forks
39
Bundled files
1
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Pdf Text Extractor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/pdf-text-extractor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/pdf-text-extractor .claude/skills/pdf-text-extractor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pdf Text Extractor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pdf Text Extractor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pdf Text Extractor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PDF Text Extractor

Triggers & routing

  • Trigger: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF.
  • Use when: queries.md 设置 evidence_mode: fulltext（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。

Optionally collect full-text snippets to deepen evidence beyond abstracts.

This skill is intentionally conservative: in many survey runs, abstract/snippet mode is enough and avoids heavy downloads.

Inputs

  • papers/core_set.csv (expects paper_id, title, and ideally pdf_url/arxiv_id/url)
  • Optional: outline/mapping.tsv (to prioritize mapped papers)

Outputs

  • papers/fulltext_index.jsonl (one record per attempted paper)
  • Side artifacts:
    • papers/pdfs/<paper_id>.pdf (cached downloads)
    • papers/fulltext/<paper_id>.txt (extracted text)

Decision: evidence mode

  • queries.md can set evidence_mode: "abstract" | "fulltext".
    • abstract (default template): do not download; write an index that clearly records skipping.
    • fulltext: download PDFs (when possible) and extract text to papers/fulltext/.

Local PDFs Mode

When you cannot/should not download PDFs (restricted network, rate limits, no permission), provide PDFs manually and run in “local PDFs only” mode.

  • PDF naming convention: papers/pdfs/<paper_id>.pdf where <paper_id> matches papers/core_set.csv.
  • Set - evidence_mode: "fulltext" in queries.md.
  • Run: uv run python .codex/skills/pdf-text-extractor/scripts/run.py --workspace <workspace> --local-pdfs-only

If PDFs are missing, the script writes a to-do list:

  • output/MISSING_PDFS.md (human-readable summary)
  • papers/missing_pdfs.csv (machine-readable list)

Workflow (heuristic)

  1. Read papers/core_set.csv.
  2. If outline/mapping.tsv exists, prioritize mapped papers first.
  3. For each selected paper (fulltext mode):
    • resolve pdf_url (use pdf_url, else derive from arxiv_id/url when possible)
    • download to papers/pdfs/<paper_id>.pdf if missing
    • extract a reasonable prefix of text to papers/fulltext/<paper_id>.txt
    • append/update a JSONL record in papers/fulltext_index.jsonl with status + stats
  4. Never overwrite existing extracted text unless explicitly requested (delete the .txt to re-extract).

Quality checklist

  • papers/fulltext_index.jsonl exists and is non-empty.
  • If evidence_mode: "fulltext": at least a small but non-trivial subset has extracted text (strict mode blocks if extraction coverage is near-zero).
  • If evidence_mode: "abstract": the index covers every papers/core_set.csv paper and every record clearly reflects skip_mode_abstract (no downloads attempted). fulltext_max_papers does not truncate this zero-download index.

Script

Quick Start

  • uv run python .codex/skills/pdf-text-extractor/scripts/run.py --help
  • uv run python .codex/skills/pdf-text-extractor/scripts/run.py --workspace <workspace>

All Options

  • --max-papers <n>: cap number of papers processed (can be overridden by queries.md)
  • --max-pages <n>: extract at most N pages per PDF
  • --min-chars <n>: minimum extracted chars to count as OK
  • --sleep <sec>: delay between downloads
  • --local-pdfs-only: do not download; only use papers/pdfs/<paper_id>.pdf if present
  • queries.md supports: evidence_mode, fulltext_max_papers, fulltext_max_pages, fulltext_min_chars

Examples

  • Abstract mode (no downloads):
    • Set - evidence_mode: "abstract" in queries.md, then run the script (it will emit papers/fulltext_index.jsonl with skip statuses)
  • Fulltext mode with local PDFs only:
    • Set - evidence_mode: "fulltext" in queries.md, put PDFs under papers/pdfs/, then run: uv run python .codex/skills/pdf-text-extractor/scripts/run.py --workspace <workspace> --local-pdfs-only
  • Fulltext mode with smaller budget:
    • uv run python .codex/skills/pdf-text-extractor/scripts/run.py --workspace <workspace> --max-papers 20 --max-pages 4 --min-chars 1200

Notes

  • Downloads are cached under papers/pdfs/; extracted text is cached under papers/fulltext/.
  • The script does not overwrite existing extracted text unless you delete the .txt file.

Troubleshooting

Issue: no PDFs are available to download

Fix:

  • Use evidence_mode: abstract (default) or provide local PDFs under papers/pdfs/ and rerun with --local-pdfs-only.

Issue: extracted text is empty/garbled

Fix:

  • Try a different extraction backend if supported; otherwise mark the paper as abstract evidence level and avoid strong fulltext claims.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pdf Text Extractor AI skill do?

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`.

Why use Pdf Text Extractor on TypingMind?

Because you install it once and use it with any model. Pdf Text Extractor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pdf Text Extractor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/pdf-text-extractor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pdf Text Extractor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pdf Text Extractor?

As many as you like. As long as a model supports skills, you can use Pdf Text Extractor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pdf Text Extractor AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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