jsonlite

jsonlite is a skill for Claude Code, Codex from LeoLin990405/r-analytics-skill. It costs 22 tokens per session (678 once invoked), scanned A, original, MIT.

An R package for reading, writing, parsing, and converting JSON, the text format commonly used for structured data and web APIs.

In plain words
What is it for?
Loading JSON from files, strings, or URLs; converting data frames; flattening nested data; and writing JSON output.
Why use it?
It removes the need to manually handle JSON strings and nested data structures in R.

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/jsonlite
Any agent
npx skills add LeoLin990405/r-analytics-skill --skill jsonlite
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 jsonlite

README.md
[![agentmods](https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/jsonlite.svg)](https://agentmods.dev/skills/leolin990405/r-analytics-skill/jsonlite)
Your own site
<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/jsonlite"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/jsonlite.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 678 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 $0.00022 $0.00678
Opus 5 $0.00011 $0.00339
Sonnet 5 $0.00004 $0.00136
Haiku 4.5 $0.00002 $0.00068

Measured 4d ago against content hash 48074b3ad64e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

jsonlite 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 4d 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-formats/jsonlite/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

jsonlite

JSON parsing and generation.

Read JSON

library(jsonlite)

# From file
data <- fromJSON("file.json")

# From string
data <- fromJSON('{"name": "John", "age": 30}')

# From URL
data <- fromJSON("https://api.example.com/data")

# Options
data <- fromJSON("file.json",
  simplifyVector = TRUE,
  simplifyDataFrame = TRUE,
  simplifyMatrix = TRUE,
  flatten = TRUE
)

Write JSON

# To string
json <- toJSON(df)
json <- toJSON(df, pretty = TRUE)

# To file
write_json(df, "output.json")
write_json(df, "output.json", pretty = TRUE)

# Options
json <- toJSON(df,
  pretty = TRUE,
  auto_unbox = TRUE,
  na = "null",
  null = "null",
  digits = 4,
  Date = "ISO8601"
)

Data Frame Conversion

# Data frame to JSON
df <- data.frame(x = 1:3, y = c("a", "b", "c"))
toJSON(df)
# [{"x":1,"y":"a"},{"x":2,"y":"b"},{"x":3,"y":"c"}]

# Row-oriented
toJSON(df, dataframe = "rows")

# Column-oriented
toJSON(df, dataframe = "columns")
# {"x":[1,2,3],"y":["a","b","c"]}

# Values only
toJSON(df, dataframe = "values")
# [[1,"a"],[2,"b"],[3,"c"]]

Nested JSON

# Flatten nested
data <- fromJSON("nested.json", flatten = TRUE)

# Access nested elements
data$nested$field
data[["nested"]][["field"]]

# Unnest with tidyr
library(tidyr)
df %>% unnest_wider(nested_col)

Streaming

# Stream large files
stream_in(file("large.json"))

# Stream out
con <- file("output.json", open = "wb")
stream_out(df, con)
close(con)

# NDJSON (newline-delimited)
stream_in(file("data.ndjson"))
stream_out(df, file("output.ndjson"))

API Requests

# GET request
data <- fromJSON("https://api.example.com/data")

# With httr
library(httr)
resp <- GET("https://api.example.com/data")
data <- fromJSON(content(resp, "text"))

# POST with JSON body
resp <- POST(
  "https://api.example.com/data",
  body = toJSON(list(key = "value")),
  content_type_json()
)

Validation

# Validate JSON
validate('{"valid": true}')  # TRUE
validate('{invalid}')        # FALSE

# Minify
minify('{ "x" : 1 }')  # {"x":1}

# Prettify
prettify('{"x":1}')

Read the full file on GitHub · 138 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. 4d ago First seen · 138 lines · 22 tokens per session scan A 48074b3ad64e

Subscribe to this mod's changes

jsonlite is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 678 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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