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 agentmods add skills/leolin990405/r-analytics-skill/stringrnpx skills add LeoLin990405/r-analytics-skill --skill stringrgit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWhat 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 | $0.00024 | $0.00784 |
| Opus 5 | $0.00012 | $0.00392 |
| Sonnet 5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
stringr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stringr
Consistent string manipulation.
Basic Operations
library(stringr)
# Length
str_length("hello")
# Combine
str_c("a", "b", "c")
str_c("a", "b", sep = "-")
str_c(c("a", "b"), collapse = ", ")
# Subset
str_sub("hello", 1, 3) # "hel"
str_sub("hello", -2) # "lo"
# Case
str_to_upper("hello")
str_to_lower("HELLO")
str_to_title("hello world")
str_to_sentence("hello world")
# Trim/pad
str_trim(" hello ")
str_squish(" hello world ")
str_pad("5", width = 3, pad = "0") # "005"
# Truncate
str_trunc("hello world", width = 8)
Pattern Matching
# Detect
str_detect("hello", "ell")
str_detect(x, "^a") # Starts with
str_detect(x, "z$") # Ends with
# Locate
str_locate("hello", "l")
str_locate_all("hello", "l")
# Count
str_count("hello", "l")
# Which
str_which(x, "pattern")
str_subset(x, "pattern")
Extract and Replace
# Extract
str_extract("abc123def", "\\d+") # "123"
str_extract_all("a1b2c3", "\\d") # c("1", "2", "3")
# Match groups
str_match("abc123", "(\\w+)(\\d+)")
str_match_all(x, "(\\w+)=(\\d+)")
# Replace
str_replace("hello", "l", "L") # First
str_replace_all("hello", "l", "L") # All
str_replace_all(x, c("a" = "1", "b" = "2"))
# Remove
str_remove("hello", "l")
str_remove_all("hello", "l")
Split and Join
# Split
str_split("a,b,c", ",")
str_split_fixed("a,b,c", ",", n = 2)
str_split_1("a,b,c", ",") # Returns vector
# Join
str_flatten(c("a", "b", "c"), collapse = ", ")
str_flatten_comma(c("a", "b", "c"))
Regex
# Literal string
fixed("$100")
# Case insensitive
regex("hello", ignore_case = TRUE)
# Boundary
str_extract_all("hello world", boundary("word"))
# Common patterns
"\\d" # Digit
"\\w" # Word character
"\\s" # Whitespace
"." # Any character
"^" # Start
"$" # End
"+" # One or more
"*" # Zero or more
"?" # Zero or one
"{n}" # Exactly n
"{n,m}" # Between n and m
"[abc]" # Character class
"[^abc]" # Negated class
"()" # Group
"|" # Or
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.
- 3d ago First seen · 136 lines · 24 tokens per session scan A f98e964d800c
stringr is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 784 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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