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PDF

当用户需要对PDF文件执行任何操作时使用此技能,包括读取或提取文本/表格、合并、拆分、旋转页面、添加水印、创建新PDF、填写表单、加密/解密、提取图像以及扫描PDF的OCR识别。

PDF 是什么?

PDF处理技能是一个全面的工具包,用于自动化任何与PDF相关的任务。它涵盖多种操作场景,包括文本和表格提取(使用pdfplumber)、PDF合并与拆分(使用pypdf)、PDF创建(使用reportlab)、水印添加、页面旋转、密码保护、图像提取以及扫描文档的OCR(使用pytesseract)。该技能支持Python库和命令行工具(如qpdf、pdftotext、pdftk),因此适用于需要生成或操作PDF文件的Claude Code、Cursor或Codex等AI代理。其主要优势在于提供即用型代码示例、最佳实践(例如避免在ReportLab中使用Unicode上标/下标字符),并可参考专门的forms.md来填写PDF表单。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/pdf

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

PDF 处理指南

概览

本指南涵盖了使用 Python 库和命令行工具进行基本 PDF 处理的操作。有关高级功能、JavaScript 库和详细示例,请参阅 reference.md。如果您需要填写 PDF 表单,请阅读 forms.md 并遵循其说明。

快速入门

from pypdf import PdfReader, PdfWriter

# 读取 PDF
reader = PdfReader("document.pdf")
print(f"页数: {len(reader.pages)}")

# 提取文本
text = ""
for page in reader.pages:
    text += page.extract_text()

Python 库

pypdf - 基本操作

合并 PDF

from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)

拆分 PDF

reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)

提取元数据

reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"标题: {meta.title}")
print(f"作者: {meta.author}")
print(f"主题: {meta.subject}")
print(f"创建者: {meta.creator}")

旋转页面

reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # 顺时针旋转 90 度
writer.add_page(page)

with open("rotated.pdf", "wb") as output:
    writer.write(output)

pdfplumber - 文本和表格提取

保持布局提取文本

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)

提取表格

with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"第 {i+1} 页的表格 {j+1}:")
            for row in table:
                print(row)

高级表格提取

import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table:  # 检查表格是否不为空
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# 合并所有表格
if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)

reportlab - 创建 PDF

基本 PDF 创建

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter

# 添加文本
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")

# 添加线条
c.line(100, height - 140, 400, height - 140)

# 保存
c.save()

创建多页 PDF

from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

# 添加内容
title = Paragraph("报告标题", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))

body = Paragraph("这是报告正文。" * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())

# 第 2 页
story.append(Paragraph("第 2 页", styles['Heading1']))
story.append(Paragraph("第 2 页的内容", styles['Normal']))

# 构建 PDF
doc.build(story)

下标和上标

重要提示:切勿在 ReportLab PDF 中使用 Unicode 下标/上标字符(₀₁₂₃₄₅₆₇₈₉, ⁰¹²³⁴⁵⁶⁷⁸⁹)。内置字体不包含这些字形,会导致它们渲染为实心黑框。

相反,请在段落对象中使用 ReportLab 的 XML 标记标签:

from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet

styles = getSampleStyleSheet()

# 下标:使用 <sub> 标签
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])

# 上标:使用 <super> 标签
squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])

对于画布绘制的文本(非 Paragraph 对象),请手动调整字体大小和位置,而不是使用 Unicode 下标/上标。

命令行工具

pdftotext (poppler-utils)

# 提取文本
pdftotext input.pdf output.txt

# 保持布局提取文本
pdftotext -layout input.pdf output.txt

# 提取特定页面
pdftotext -f 1 -l 5 input.pdf output.txt  # 第 1-5 页

qpdf

# 合并 PDF
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# 拆分页面
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf

# 旋转页面
qpdf input.pdf output.pdf --rotate=+90:1  # 将第 1 页旋转 90 度

# 移除密码
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf

pdftk (如果可用)

# 合并
pdftk file1.pdf file2.pdf cat output merged.pdf

# 拆分
pdftk input.pdf burst

# 旋转
pdftk input.pdf rotate 1east output rotated.pdf

常见任务

从扫描版 PDF 提取文本

# 需要: uv pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path

# 将 PDF 转换为图像
images = convert_from_path('scanned.pdf')

# 对每页进行 OCR
text = ""
for i, image in enumerate(images):
    text += f"第 {i+1} 页:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"

print(text)

添加水印

from pypdf import PdfReader, PdfWriter

# 创建水印(或加载现有水印)
watermark = PdfReader("watermark.pdf").pages[0]

# 应用到所有页面
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

with open("watermarked.pdf", "wb") as output:
    writer.write(output)

提取图像

# 使用 pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix

# 这会将所有图像提取为 output_prefix-000.jpg, output_prefix-001.jpg 等

密码保护

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

# 添加密码
writer.encrypt("userpassword", "ownerpassword")

with open("encrypted.pdf", "wb") as output:
    writer.write(output)

快速参考

任务最佳工具命令/代码
合并 PDFpypdfwriter.add_page(page)
拆分 PDFpypdf每个文件一页
提取文本pdfplumberpage.extract_text()
提取表格pdfplumberpage.extract_tables()
创建 PDFreportlabCanvas 或 Platypus
命令行合并qpdfqpdf --empty --pages ...
扫描 PDF 的 OCRpytesseract先转换为图像
填写 PDF 表单pdf-lib 或 pypdf (参见 forms.md)参见 forms.md

后续步骤

  • 有关高级 pypdfium2 用法,请查看 reference.md
  • 有关 JavaScript 库 (pdf-lib),请查看 reference.md
  • 如果您需要填写 PDF 表单,请遵循 forms.md 中的说明
  • 有关故障排除指南,请查看 reference.md

本技能由 Anthropic 创建和维护。除前言元数据和 reference.md/forms.md 链接的大小写外(上游使用大写),此处未经修改地引入;有关条款请参见 LICENSE.txt。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

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