Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cxcscmu/SkillLearnBench --skill fuzzy-fund-searchgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cxcscmu/skilllearnbench/fuzzy-fund-search)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/fuzzy-fund-search"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/fuzzy-fund-search.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01796 |
| Opus 5 | $0.00010 | $0.00898 |
| Sonnet 5 | $0.00004 | $0.00359 |
| Haiku 4.5 | $0.00002 | $0.00180 |
Grade A, and why
fuzzy-fund-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fuzzy Name Search for Hedge Funds
Overview
Fund names in the COVERPAGE.tsv may not match exactly what you're searching for. Fuzzy matching helps find the best match when you have approximate or partial names.
Libraries
Using fuzzywuzzy
pip install fuzzywuzzy python-Levenshtein
Using difflib (built-in)
No installation needed - Python standard library
Search Methods
Method 1: Using fuzzywuzzy (Recommended)
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import pandas as pd
def fuzzy_search_fund(search_term, coverpage_df, threshold=70):
"""
Find funds matching a search term using fuzzy matching
Args:
search_term: Partial or approximate fund name (e.g., "renaissance technologies")
coverpage_df: DataFrame from COVERPAGE.tsv
threshold: Minimum match score (0-100)
Returns:
DataFrame of matches sorted by score (highest first)
"""
fund_names = coverpage_df['FILINGMANAGER_NAME'].tolist()
# Use extractBests to get multiple matches
matches = process.extractBests(
search_term,
fund_names,
scorer=fuzz.token_sort_ratio, # Good for finding partial matches
score_cutoff=threshold
)
# Build result dataframe
results = []
for matched_name, score in matches:
fund_row = coverpage_df[coverpage_df['FILINGMANAGER_NAME'] == matched_name].iloc[0]
results.append({
'FILINGMANAGER_NAME': matched_name,
'ACCESSION_NUMBER': fund_row['ACCESSION_NUMBER'],
'match_score': score,
'REPORTCALENDARORQUARTER': fund_row['REPORTCALENDARORQUARTER']
})
return pd.DataFrame(results).sort_values('match_score', ascending=False)
# Usage Example
q3_coverpage = pd.read_csv('/root/2025-q3/COVERPAGE.tsv', sep='\t')
matches = fuzzy_search_fund("renaissance technologies", q3_coverpage, threshold=70)
print(matches.head())
# Get best match
best_match = matches.iloc[0]
accession_number = best_match['ACCESSION_NUMBER']
fund_name = best_match['FILINGMANAGER_NAME']
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 248 lines · 21 tokens per session scan A 1263a9c969ea
fuzzy-fund-search is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 1,796 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
portfolio
Cross-chain DeFi portfolio discovery, rebalancing suggestions, and NEAR Intent construction. Activates when the user pastes a wallet address or asks about yield/positions/rebalancing. Bootstraps a per-user "portfolio" project, aggregates positions across all the user's addresses inside one project, and offers a…
trader-setup
One-time onboarding for the financial trader workflow — real-time alerts, position-aware relevance, decision journaling with outcome tracking. After successful setup this skill is excluded from selection until the marker file is deleted.
paired-reversal-cancellation
Reference for paired-reversal handling on GL batch tapes: an RV row whose ref-trace points at an earlier row cancels BOTH legs (the RV and the row it references) — but ONLY when the two value-dates fall within the shop's reversal settlement window; an RV that references a too-old posting is NOT a cancellation and…
triangulated-fx-rates
Reference for multi-hop / triangulated currency conversion on batch rate tapes. When a rate row carries an explicit "via" currency, the row's rate is only one leg of the conversion, and the via currency may ITSELF be quoted through another via — so the effective rate is the product of every leg, resolved by walking…
timeseries-detrending
Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.
fuzzy-name-search
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.