القائمة
How to find top fund managers heavily invested in a particular stock across all data quality issues.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/cxcscmu/SkillLearnBench --skill run2-13f-stock-searchيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | run2_13f-stock-search |
| description | How to find top fund managers heavily invested in a particular stock across all data quality issues. |
When searching for funds heavily invested in a single stock, be aware that CUSIP codes might be entered in varying cases (e.g. 69608A108 vs 69608a108), so normalizations like .str.upper() are critical.
This strategy avoids issues with case sensitivity and missing commas when parsing TITLEOFCLASS.
import pandas as pd
cusip = "69608A108"
quarter = "2025-q3"
infotable = pd.read_csv(f"/root/{quarter}/INFOTABLE.tsv", sep="\t", dtype=str)
coverpage = pd.read_csv(f"/root/{quarter}/COVERPAGE.tsv", sep="\t", dtype=str)
# Filter by CUSIP defensively
infotable["CUSIP_UPPER"] = infotable["CUSIP"].str.upper()
stock_holders = infotable[infotable["CUSIP_UPPER"] == cusip].copy()
# Sum the VALUE by ACCESSION_NUMBER
stock_holders["VALUE"] = stock_holders["VALUE"].astype(float)
fund_sums = stock_holders.groupby("ACCESSION_NUMBER")["VALUE"].sum().reset_index()
# Sort to get top 3
top_funds = fund_sums.sort_values(by="VALUE", ascending=False).head(3)
# Merge back to COVERPAGE to obtain FILINGMANAGER_NAME
merged = pd.merge(top_funds, coverpage[["ACCESSION_NUMBER", "FILINGMANAGER_NAME"]], on="ACCESSION_NUMBER", how="left")
print(merged[["FILINGMANAGER_NAME", "VALUE"]])