yipng05-max/-skills/pdf-bib-import

Claude Code and agent skill by yipng05-max/-skills

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

将一个文件夹中的多篇 PDF 论文批量提取题录信息(标题、作者、年份、期刊、卷期页码、DOI、摘要),并导入飞书多维表格。当用户需要把一批 PDF 论文的书目信息、题录信息汇总到飞书 Base / 多维表格时触发。关键词:PDF、论文、题录、书目、批量导入、飞书多维表格。

Install

npx vskill@latest install yipng05-max/-skills/pdf-bib-import

SKILL.md

PDF 题录批量导入飞书多维表格

核心原则(必须遵守)

成本控制:数据提取必须用脚本完成,禁止用 AI 逐篇读取 PDF——每次 AI 读取都消耗大量 token。
正确流程:写脚本提取 → 人工/脚本校对 → 脚本批量写入飞书,AI 只负责生成脚本和配置飞书结构。

Step 1:脚本提取 PDF 题录

1.1 生成提取脚本

在 /tmp 下生成 extract_bib.py,利用 pdftotext(/opt/homebrew/bin/pdftotext)批量提取:

python
#!/usr/bin/env python3
"""
extract_bib.py — 从 PDF 目录批量提取题录,输出 bib_data.json
用法:python3 extract_bib.py <pdf_dir>
"""
import os, sys, json, subprocess, re
def run(cmd):
return subprocess.run(cmd, capture_output=True, text=True).stdout.strip()
def extract_pdf_meta(path):
info = run(["/opt/homebrew/bin/pdfinfo", path])
meta = {}
for line in info.splitlines():
if ":" in line:
k, _, v = line.partition(":")
meta[k.strip()] = v.strip()
text = run(["/opt/homebrew/bin/pdftotext", "-l", "2", path, "-"])
return meta, text
def guess_fields(meta, text):
"""从 pdfinfo + 前两页文字中猜测题录字段,返回 dict。"""
lines = [l.strip() for l in text.splitlines() if l.strip()]
return {
"论文题目": meta.get("Title", ""),
"作者": meta.get("Author", ""),
"发表年份": None,
"期刊名称": "",
"卷期页码": "",
"DOI": "",
"摘要": "",
"_raw_text_preview": "\n".join(lines[:40]), # 供人工核查
}
def main():
pdf_dir = sys.argv[1] if len(sys.argv) > 1 else "."
results = []
for fname in sorted(os.listdir(pdf_dir)):
if not fname.lower().endswith(".pdf"):
continue
fpath = os.path.join(pdf_dir, fname)
meta, text = extract_pdf_meta(fpath)
rec = guess_fields(meta, text)
rec["_filename"] = fname
results.append(rec)
print(f" ✓ {fname}")
out = "/tmp/bib_data.json"
with open(out, "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print(f"\n已写出 {len(results)} 条记录 → {out}")
print("请检查 bib_data.json,补全缺失字段后再执行 Step 3 导入。")
if __name__ == "__main__":
main()

运行:

bash
python3 /tmp/extract_bib.py "/path/to/pdf_folder"
1.2 校对 JSON(关键步骤)

pdftotext 能提取机器可读 PDF 的文字,但有些 PDF:

  • 扫描件:文字为空,需手动填写
  • 元数据为空:需从 _raw_text_preview 字段中判断并手动补全

打开 /tmp/bib_data.json,检查并补全各字段,删除 _raw_text_preview 和 _filename。


Step 2:创建飞书 Base 结构

前置条件: 先阅读 `../lark-shared/SKILL.md` 了解认证规则。
2.1 创建 Base
bash
lark-cli base +base-create --name "论文题录数据库"
# 记录返回的 base_token
2.2 获取默认表并重命名
bash
lark-cli base +table-list --base-token <BASE_TOKEN>
lark-cli base +table-update --base-token <BASE_TOKEN> --table-id <TABLE_ID> --json '{"name":"论文题录"}'
2.3 配置字段

先重命名默认"文本"字段,再删除多余默认字段,最后创建所需字段:

bash
# 查看现有字段(记录各字段 id)
lark-cli base +field-list --base-token <BASE_TOKEN> --table-id <TABLE_ID>
# 将默认文本字段重命名为"论文题目"
lark-cli base +field-update --base-token <BASE_TOKEN> --table-id <TABLE_ID> \
--field-id <TEXT_FIELD_ID> --json '{"type":"text","name":"论文题目","style":{"type":"plain"}}'
# 删除多余默认字段(日期、附件、单选)
lark-cli base +field-delete --base-token <BASE_TOKEN> --table-id <TABLE_ID> --field-id <ID> --yes
# 创建其余字段(串行执行)
cd /tmp
for field in \
'{"type":"text","name":"作者","style":{"type":"plain"}}' \
'{"type":"number","name":"发表年份"}' \
'{"type":"text","name":"期刊名称","style":{"type":"plain"}}' \
'{"type":"text","name":"卷期页码","style":{"type":"plain"}}' \
'{"type":"text","name":"DOI","style":{"type":"plain"}}' \
'{"type":"text","name":"摘要","style":{"type":"plain"}}'; do
echo "$field" > field.json
lark-cli base +field-create --base-token <BASE_TOKEN> --table-id <TABLE_ID> --json @field.json
sleep 0.3
done

Step 3:脚本批量写入记录

3.1 生成导入脚本

生成 /tmp/import_to_lark.sh,从 /tmp/bib_data.json 读取记录并逐条写入:

bash
cat << 'PYEOF' > /tmp/import_to_lark.py
#!/usr/bin/env python3
"""
import_to_lark.py — 从 bib_data.json 批量写入飞书 Base
用法:python3 import_to_lark.py <BASE_TOKEN> <TABLE_ID> [bib_data.json]
"""
import json, sys, subprocess, time, os
def upsert(base_token, table_id, record):
tmp = "/tmp/_rec.json"
# 去除空值字段(避免写入空字符串覆盖)
payload = {k: v for k, v in record.items()
if not k.startswith("_") and v not in (None, "", 0)}
# 年份是数字,0 代表未知,保持过滤
with open(tmp, "w", encoding="utf-8") as f:
json.dump(payload, f, ensure_ascii=False)
result = subprocess.run(
["lark-cli", "base", "+record-upsert",
"--base-token", base_token,
"--table-id", table_id,
"--json", "@_rec.json"],
capture_output=True, text=True, cwd="/tmp"
)
return json.loads(result.stdout)
def main():
base_token = sys.argv[1]
table_id = sys.argv[2]
data_file = sys.argv[3] if len(sys.argv) > 3 else "/tmp/bib_data.json"
with open(data_file, encoding="utf-8") as f:
records = json.load(f)
ok, fail = 0, 0
for i, rec in enumerate(records, 1):
title = rec.get("论文题目", rec.get("_filename", f"record-{i}"))
resp = upsert(base_token, table_id, rec)
if resp.get("ok"):
ok += 1
print(f" [{i}/{len(records)}] ✓ {title[:60]}")
else:
fail += 1
print(f" [{i}/{len(records)}] ✗ {title[:60]}: {resp}")
time.sleep(0.5) # 避免写入冲突
print(f"\n完成:{ok} 成功 / {fail} 失败")
if __name__ == "__main__":
main()
PYEOF
python3 /tmp/import_to_lark.py <BASE_TOKEN> <TABLE_ID> /tmp/bib_data.json

常见问题

问题解决方案
pdftotext 提取为空(扫描件)手动填写 bib_data.json,或用 OCR 工具
PDF 元数据乱码 / 错误忽略 meta,从 _raw_text_preview 手工提取
lark-cli --json @file 报路径错误必须 cd /tmp 后再执行,使用相对路径 @_rec.json
写入冲突 1254291增大 time.sleep() 到 1 秒
字段不存在 1254045检查字段名拼写,与 +field-list 返回结果一致

成本控制提示

  • 不要用 AI 逐篇读取 PDF:49 篇 PDF 用 AI 提取 ≈ $10,用脚本 ≈ $0
  • 数据提取 → 人工校对 → 脚本写入,AI 只生成脚本
  • 遇到 pdftotext 无法提取的字段,集中人工补全 JSON,再统一导入

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
281
Repository forks
24
7-day trend score
28

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/yipng05-max/-skills/pdf-bib-import/badge)](https://verified-skill.com/skills/yipng05-max/-skills/pdf-bib-import)

Something wrong with this skill or its page? Tell us.