Agent skill

Fuzzy Match

by benchflow-ai in benchflow-ai/skillsbench

A toolkit for fuzzy string matching and data reconciliation.

MITAuto-check passedBusiness, Finance & HR

Install Fuzzy Match

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .claude/skills/fuzzy-match && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .claude/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .agents/skills/fuzzy-match && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .agents/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .cursor/skills/fuzzy-match && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .cursor/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/invoice-fraud-detection/environment/skills/fuzzy-match

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .gemini/skills/fuzzy-match && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .gemini/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .github/skills/fuzzy-match && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .github/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench fuzzy-match --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/invoice-fraud-detection/environment/skills/fuzzy-match .opencode/skills/fuzzy-match && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "fuzzy-match" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/invoice-fraud-detection/environment/skills/fuzzy-match into .opencode/skills/fuzzy-match/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzy-match", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: fuzzy-match
GitHub stars: 1.8k
Token cost: ~794 tokens
SKILL.md length: 106 words
Files: 1
Skills in repo: 178
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A toolkit for fuzzy string matching and data reconciliation.

Tasks that involve Accounting and bookkeeping
SKILL.md covers Overview, Quick Start, Python Libraries and Common Patterns
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Fuzzy Match is an agent skill from benchflow-ai/skillsbench. A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.

Its SKILL.md is about 790 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Accounting and bookkeeping. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is MIT.

When your agent uses it

Tasks that involve Accounting and bookkeeping

Example prompts

“/fuzzy-match”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fuzzy Match loads about 794 tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 106 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~55
When it runs · the whole SKILL.md, loaded when a task matches: ~794

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its MIT licence (© benchflow-ai). 106 words, ~794 tokens.

Download SKILL.mdSave it as .claude/skills/fuzzy-match/SKILL.md (or your agent's skills folder).

name: fuzzy-match
description: A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.
license: MIT

Fuzzy Matching Guide

Overview

This skill provides methods to compare strings and find the best matches using Levenshtein distance and other similarity metrics. It is essential when joining datasets on string keys that are not identical.

Quick Start

python

from difflib import SequenceMatcher

def similarity(a, b):
    return SequenceMatcher(None, a, b).ratio()

print(similarity("Apple Inc.", "Apple Incorporated"))
# Output: 0.7...

Python Libraries

difflib (Standard Library)

The difflib module provides classes and functions for comparing sequences.

Basic Similarity

python

from difflib import SequenceMatcher

def get_similarity(str1, str2):
    """Returns a ratio between 0 and 1."""
    return SequenceMatcher(None, str1, str2).ratio()

# Example
s1 = "Acme Corp"
s2 = "Acme Corporation"
print(f"Similarity: {get_similarity(s1, s2)}")

Finding Best Match in a List

python

from difflib import get_close_matches

word = "appel"
possibilities = ["ape", "apple", "peach", "puppy"]
matches = get_close_matches(word, possibilities, n=1, cutoff=0.6)
print(matches)
# Output: ['apple']

rapidfuzz (Recommended for Performance)

If rapidfuzz is available (pip install rapidfuzz), it is much faster and offers more metrics.

python

from rapidfuzz import fuzz, process

# Simple Ratio
score = fuzz.ratio("this is a test", "this is a test!")
print(score)

# Partial Ratio (good for substrings)
score = fuzz.partial_ratio("this is a test", "this is a test!")
print(score)

# Extraction
choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
best_match = process.extractOne("new york jets", choices)
print(best_match)
# Output: ('New York Jets', 100.0, 1)

Common Patterns

Normalization before Matching

Always normalize strings before comparing to improve accuracy.

python

import re

def normalize(text):
    # Convert to lowercase
    text = text.lower()
    # Remove special characters
    text = re.sub(r'[^\w\s]', '', text)
    # Normalize whitespace
    text = " ".join(text.split())
    # Common abbreviations
    text = text.replace("limited", "ltd").replace("corporation", "corp")
    return text

s1 = "Acme  Corporation, Inc."
s2 = "acme corp inc"
print(normalize(s1) == normalize(s2))

Entity Resolution

When matching a list of dirty names to a clean database:

python

clean_names = ["Google LLC", "Microsoft Corp", "Apple Inc"]
dirty_names = ["google", "Microsft", "Apple"]

results = {}
for dirty in dirty_names:
    # simple containment check first
    match = None
    for clean in clean_names:
        if dirty.lower() in clean.lower():
            match = clean
            break

    # fallback to fuzzy
    if not match:
        matches = get_close_matches(dirty, clean_names, n=1, cutoff=0.6)
        if matches:
            match = matches[0]

    results[dirty] = match

Files

Just SKILL.md in tasks/invoice-fraud-detection/environment/skills/fuzzy-match of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Fuzzy Match next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fuzzy Match compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Fuzzy Match this skillbenchflow-ai/skillsbench	1.8k	—	~794	Automated safety check: Pass	MIT	2 mo ago
Sync Upstreamnyaruka/phonenumbers	1.6k	—	~2.8k	Automated safety check: Pass	MIT	6 days ago
Longbridge Value Investinghelsome/folio	269	2 repos	~1.2k	Automated safety check: Pass	MIT	5 days ago
Radiology Tablehuang-sir1/radiology-skills	1.9k	—	~1.3k	Automated safety check: Pass	Custom licence	17 days ago
Odoo Agency Fleet Reviewerpipe-org/mcp-odoo	420	—	~699	Automated safety check: Pass	MIT	1 mo ago
Beancount Closebex-co/beancount-io	295	—	~1.4k	Automated safety check: Pass	MIT	yesterday

Similar skills

Sync Upstream
nyaruka/phonenumbers
Sync this Go port with a new upstream google/libphonenumber release — regenerate the embedded metadata and reconcile the ported Java logic.
1.6k GitHub stars~2.8k tokensUpdated 6 days ago
Business, Finance & HRAuto-check passed
Longbridge Value Investing
helsome/folio
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies.
269 GitHub starsUsed in 2 repos~1.2k tokens
Business, Finance & HRAuto-check passed
Radiology Table
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Create/audit editable publication tables with source reconciliation; not figures or statistical inference.
1.9k GitHub stars~1.3k tokensUpdated 17 days ago
Business, Finance & HRAuto-check passed
Odoo Agency Fleet Review
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Business, Finance & HRAuto-check passed
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ERPClaw ERP Controller
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Questions about Fuzzy Match

What does Fuzzy Match do?

A toolkit for fuzzy string matching and data reconciliation. Fuzzy Match is an agent skill from benchflow-ai/skillsbench. A toolkit for fuzzy string matching and data reconciliation.

When should I use Fuzzy Match?

Fuzzy Match fits situations like: tasks that involve Accounting and bookkeeping.

How do I install Fuzzy Match in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a claude-code`. Or copy the skill folder (tasks/invoice-fraud-detection/environment/skills/fuzzy-match in benchflow-ai/skillsbench) into .claude/skills/fuzzy-match in your project. Claude Code loads it when a task matches its description.

How do I install Fuzzy Match in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a codex`. Or copy the skill folder (tasks/invoice-fraud-detection/environment/skills/fuzzy-match in benchflow-ai/skillsbench) into .agents/skills/fuzzy-match in your project. Codex loads it when a task matches its description.

Can I use Fuzzy Match in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill fuzzy-match -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fuzzy-match, .gemini/skills/fuzzy-match, .github/skills/fuzzy-match and .opencode/skills/fuzzy-match in your project.

What does Fuzzy Match need to run?

SKILL.md names no scripts, command-line tools or credentials: Fuzzy Match is instructions for the agent only. Our summary lists: Python 3.

Does Fuzzy Match access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fuzzy Match safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fuzzy Match use?

Fuzzy Match is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fuzzy Match use?

About 794 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fuzzy Match?

Skills that share tags, products or a category with Fuzzy Match: Sync Upstream (nyaruka/phonenumbers, 1.6k stars), Longbridge Value Investing (helsome/folio, 269 stars), Radiology Table (huang-sir1/radiology-skills, 1.9k stars) and Odoo Agency Fleet Review (erpipe-org/mcp-odoo, 420 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fuzzy Match?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Fuzzy Match

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Fuzzy Matching Guide

Overview

Quick Start

Python Libraries

difflib (Standard Library)

Basic Similarity

Finding Best Match in a List

rapidfuzz (Recommended for Performance)

Common Patterns

Normalization before Matching

Entity Resolution

Files

Compare with similar skills

Similar skills

More from benchflow-ai/skillsbench

Related

Questions about Fuzzy Match