菜单
A skill for finding best string matches in datasets using fuzzy matching libraries like fuzzywuzzy or rapidfuzz.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/cxcscmu/SkillLearnBench --skill fuzzy-string-matching命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Handles reading, populating, and saving .docx files using the python-docx library. Use this skill for any tasks involving template filling or modifying Word documents.
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
This skill includes search capability in 13F, such as fuzzy search a fund information using possibly inaccurate name, or fuzzy search a stock cusip info using its name.
基于 SOC 职业分类
正在显示 SKILL.md
| name | fuzzy-string-matching |
| description | A skill for finding best string matches in datasets using fuzzy matching libraries like fuzzywuzzy or rapidfuzz. |
This skill demonstrates how to use fuzzy string matching to find company or fund names in a dataset.
fuzzywuzzy or rapidfuzzpython-Levenshtein (optional but recommended for speed)pip install rapidfuzz
import pandas as pd
from rapidfuzz import process, fuzz
df = pd.read_csv('data.tsv', sep='\t')
names = df['NAME_COLUMN'].dropna().unique()
# Find the best match
query = "Renaissance Technologies"
best_match = process.extractOne(query, names, scorer=fuzz.token_sort_ratio)
print(f"Best match: {best_match[0]} with score {best_match[1]}")