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/feathernpx skills add LeoLin990405/r-analytics-skill --skill feathergit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/feather)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/feather"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/feather.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00026 | $0.00320 |
| Opus 5 | $0.00013 | $0.00160 |
| Sonnet 5 | $0.00005 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
feather scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
feather
Fast on-disk format for data frames.
Read/Write
library(feather)
# Write feather file
write_feather(df, "data.feather")
# Read feather file
df <- read_feather("data.feather")
# Read specific columns
df <- read_feather("data.feather", columns = c("col1", "col2"))
Metadata
# Get metadata without reading data
meta <- feather_metadata("data.feather")
meta$dim # Dimensions
meta$types # Column types
meta$path # File path
With Arrow
library(arrow)
# Modern replacement using arrow
write_feather(df, "data.feather")
df <- read_feather("data.feather")
# Arrow table
tbl <- arrow_table(df)
write_feather(tbl, "data.feather")
Performance Tips
# Feather is column-oriented
# Best for: wide data, column selection
# Fast for: reading subsets of columns
# Read only needed columns
df <- read_feather("data.feather",
columns = c("id", "value"))
Cross-Language
# Python (pandas)
import pandas as pd
df = pd.read_feather("data.feather")
df.to_feather("data.feather")
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.
- 5d ago First seen · 69 lines · 26 tokens per session scan A 2ed5118d6761
feather is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 320 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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