القائمة
How to correctly and robustly search for a fund manager's accession number in SEC 13F COVERPAGE.tsv.
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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-fund-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-fund-search |
| description | How to correctly and robustly search for a fund manager's accession number in SEC 13F COVERPAGE.tsv. |
A critical first step in analyzing 13F data is identifying a fund manager's unique ACCESSION_NUMBER. Since names can be slightly misspelled or appended with corporate entities (like "LLC", "LP"), searching requires flexibility.
grepThe fastest and most reliable way to find the accession number is often via the command line using grep -i (case-insensitive search).
# Search for Renaissance Technologies in Q3 2025
grep -i "renaissance technologies" /root/2025-q3/COVERPAGE.tsv
pandasWhen writing a script, use pandas with .str.contains rather than exact matching.
import pandas as pd
quarter = "2025-q3"
keywords = "BERKSHIRE HATHAWAY"
# Use dtype=str to avoid dropping leading zeros in IDs and to handle missing values cleanly
df = pd.read_csv(f"/root/{quarter}/COVERPAGE.tsv", sep="\t", dtype=str)
# Filter out amendments if you only want the original or final holdings report
df = df[df["REPORTTYPE"].str.contains("HOLDINGS REPORT", na=False)]
matches = df[df["FILINGMANAGER_NAME"].str.contains(keywords, case=False, na=False)]
print(matches[["ACCESSION_NUMBER", "FILINGMANAGER_NAME"]])