fuzzyjoin

fuzzyjoin is a skill for Claude Code, Codex from LeoLin990405/r-analytics-skill. It costs 29 tokens per session (843 once invoked), scanned A, original, MIT.

An R package for joining tables when matching values are not exactly equal. It can match similar text, nearby numbers or locations, overlapping ranges, and custom conditions.

In plain words
What is it for?
Use it to match customer names, link measurements within a permitted difference, join nearby geographic points, and combine date, genomic, or other intervals.
Why use it?
It helps combine records when names contain spelling differences, measurements have tolerances, coordinates are close, or ranges overlap.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/leolin990405/r-analytics-skill/fuzzyjoin
Any agent
npx skills add LeoLin990405/r-analytics-skill --skill fuzzyjoin
Clone the repo
git clone --depth 1 https://github.com/LeoLin990405/r-analytics-skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fuzzyjoin

README.md
[![agentmods](https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/fuzzyjoin.svg)](https://agentmods.dev/skills/leolin990405/r-analytics-skill/fuzzyjoin)
Your own site
<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/fuzzyjoin"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/fuzzyjoin.svg" alt="Measured on agentmods" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 843 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.00843
Opus 5 $0.00015 $0.00421
Sonnet 5 $0.00006 $0.00169
Haiku 4.5 $0.00003 $0.00084

Measured 5d ago against content hash 6dc5eca16b76, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fuzzyjoin 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 5d 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.

sub-skills/r-data/r-data-manipulation/fuzzyjoin/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fuzzyjoin

Join tables by inexact matching.

String Matching

library(fuzzyjoin)

# Regex join
regex_left_join(df1, df2, by = c("name" = "pattern"))
regex_inner_join(df1, df2, by = "name")

# Fuzzy string matching (stringdist)
stringdist_left_join(df1, df2, by = "name", max_dist = 2)
stringdist_inner_join(df1, df2, by = "name", method = "jw", max_dist = 0.1)

# Methods: "osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw"

Numeric Matching

# Difference join (within tolerance)
difference_left_join(df1, df2, by = "value", max_dist = 5)

# Distance join
distance_left_join(df1, df2, by = c("x", "y"), max_dist = 10)

Interval Matching

# Interval join (overlapping ranges)
interval_left_join(df1, df2, by = c("start", "end"))

# Genome-style intervals
genome_left_join(df1, df2, by = c("chr", "start", "end"))

Geographic Matching

# Geo join (within distance)
geo_left_join(df1, df2,
  by = c("lat", "lon"),
  max_dist = 10,
  unit = "km"
)

Custom Matching

# Fuzzy join with custom function
fuzzy_left_join(df1, df2,
  by = c("x" = "y"),
  match_fun = function(x, y) abs(x - y) < 5
)

# Multiple conditions
fuzzy_left_join(df1, df2,
  by = c("a" = "b", "c" = "d"),
  match_fun = list(`<`, `>`)
)

Semi and Anti Joins

# Fuzzy semi join (filter matches)
stringdist_semi_join(df1, df2, by = "name", max_dist = 2)

# Fuzzy anti join (filter non-matches)
stringdist_anti_join(df1, df2, by = "name", max_dist = 2)

All Join Types

# Available for all fuzzy methods:
# _inner_join, _left_join, _right_join, _full_join
# _semi_join, _anti_join

stringdist_inner_join(df1, df2, by = "name")
stringdist_full_join(df1, df2, by = "name")
regex_semi_join(df1, df2, by = "name")
difference_anti_join(df1, df2, by = "value")

Examples

# Match company names with typos
companies <- data.frame(name = c("Microsoft", "Apple Inc", "Google"))
records <- data.frame(company = c("Microsft", "Apple", "Gogle"))

stringdist_left_join(records, companies,
  by = c("company" = "name"),
  max_dist = 2
)

# Match dates within range
events <- data.frame(date = as.Date(c("2024-01-15", "2024-02-20")))
periods <- data.frame(
  start = as.Date(c("2024-01-01", "2024-02-01")),
  end = as.Date(c("2024-01-31", "2024-02-28"))
)

fuzzy_left_join(events, periods,
  by = c("date" = "start", "date" = "end"),
  match_fun = list(`>=`, `<=`)
)

Read the full file on GitHub · 120 lines

Changes

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.

  1. 5d ago First seen · 120 lines · 29 tokens per session scan A 6dc5eca16b76

Subscribe to this mod's changes

fuzzyjoin is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 843 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 2026-08-31.

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