13f Data Analysis

Organization

cxcscmu

13f-data-analysis

Analyze SEC 13-F filings data including AUM, holdings count, and cross-quarter comparisons using TSV files.

Overview

Publisher	cxcscmu
Repository	`SkillLearnBench`
Skill name	`13f-data-analysis`
Stars	91
Forks	7
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by cxcscmu on GitHub. Read the source before you install it.

Installation

Install the 13f Data Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-opus-4-6/financial-analysis/13f-data-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-opus-4-6/financial-analysis/13f-data-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench.git /tmp/SkillLearnBench
mkdir -p .claude/skills
cp -r /tmp/SkillLearnBench/skills/b1-one-shot-claude-opus-4-6/financial-analysis/13f-data-analysis .claude/skills/13f-data-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 13f Data Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads 13f Data Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so 13f Data Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SEC 13-F Data Analysis

Data Structure

13-F data is distributed as TSV files:

COVERPAGE.tsv: Filing metadata. Key columns: ACCESSION_NUMBER, FILINGMANAGER_NAME, REPORTCALENDARORQUARTER
INFOTABLE.tsv: Individual holdings. Key columns: ACCESSION_NUMBER, NAMEOFISSUER, TITLEOFCLASS, CUSIP, VALUE (in thousands $), SSHPRNAMT (shares), SSHPRNAMTTYPE
SUMMARYPAGE.tsv: Aggregated data. Key columns: ACCESSION_NUMBER, TABLEENTRYTOTAL (number of holdings), TABLEVALUETOTAL (AUM in thousands $)

Common Operations with Python/Pandas

Load data

python
import pandas as pd
cover = pd.read_csv('COVERPAGE.tsv', sep='\t')
info = pd.read_csv('INFOTABLE.tsv', sep='\t')
summary = pd.read_csv('SUMMARYPAGE.tsv', sep='\t')

Find fund by name (fuzzy)

python
from thefuzz import fuzz
cover['score'] = cover['FILINGMANAGER_NAME'].apply(lambda x: fuzz.token_sort_ratio(search_term.lower(), str(x).lower()))
best = cover.loc[cover['score'].idxmax()]
accession = best['ACCESSION_NUMBER']

Get AUM and holdings count

python
row = summary[summary['ACCESSION_NUMBER'] == accession]
aum = row['TABLEVALUETOTAL'].values[0]  # in thousands
count = row['TABLEENTRYTOTAL'].values[0]

Compare holdings across quarters

python
q2_holdings = info_q2[info_q2['ACCESSION_NUMBER'] == acc_q2][['CUSIP','VALUE','SSHPRNAMT']]
q3_holdings = info_q3[info_q3['ACCESSION_NUMBER'] == acc_q3][['CUSIP','VALUE','SSHPRNAMT']]
# Group by CUSIP (a fund may hold multiple lots of same stock)
q2_agg = q2_holdings.groupby('CUSIP')['VALUE'].sum().reset_index()
q3_agg = q3_holdings.groupby('CUSIP')['VALUE'].sum().reset_index()
merged = q3_agg.merge(q2_agg, on='CUSIP', how='outer', suffixes=('_q3','_q2')).fillna(0)
merged['change'] = merged['VALUE_q3'] - merged['VALUE_q2']
top_increases = merged.nlargest(5, 'change')

Find all holders of a specific stock (by CUSIP)

python
holders = info[info['CUSIP'] == target_cusip]
holders_agg = holders.groupby('ACCESSION_NUMBER')['VALUE'].sum().reset_index()
holders_agg = holders_agg.merge(cover[['ACCESSION_NUMBER','FILINGMANAGER_NAME']], on='ACCESSION_NUMBER')
top_holders = holders_agg.nlargest(3, 'VALUE')

Notes

VALUE column is in thousands of dollars
Multiple INFOTABLE rows can exist for same CUSIP within one filing (different share classes, put/call)
SUMMARYPAGE may have fewer rows than COVERPAGE (not all filings have summary)

Frequently asked questions

What does the 13f Data Analysis AI skill do?

Analyze SEC 13-F filings data including AUM, holdings count, and cross-quarter comparisons using TSV files.

Why use 13f Data Analysis on TypingMind?

Because you install it once and use it with any model. 13f Data Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install 13f Data Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-opus-4-6/financial-analysis/13f-data-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 13f Data Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with 13f Data Analysis?

As many as you like. As long as a model supports skills, you can use 13f Data Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 13f Data Analysis AI skill free?

Yes. It is published on GitHub by cxcscmu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

SEC 13-F Data Analysis

Data Structure

Common Operations with Python/Pandas

Load data

Find fund by name (fuzzy)

Get AUM and holdings count

Compare holdings across quarters

Find all holders of a specific stock (by CUSIP)

Notes

Frequently asked questions

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