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 skills add 0xDarkMatter/claude-mods --skill r-opsgit clone --depth 1 https://github.com/0xDarkMatter/claude-modsWrote 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/0xdarkmatter/claude-mods/r-ops)<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/r-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/r-ops/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/r-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/r-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.02238 |
| Opus 5.5 | $0.00033 | $0.00895 |
| Sonnet 5.5 | $0.00017 | $0.00448 |
| Haiku 4.5 | $0.00008 | $0.00224 |
Grade A, and why
r-ops 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 1mo 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modern R Operations
A tidyverse-first, current-best-practice reference for working in R (2024+): data analysis, statistics, visualization, and reproducible workflow. Opinionated where the community has converged, with base R and data.table flagged as the right tool when they are.
The modern R stack at a glance
| Job | Reach for | Not (anymore) |
|---|---|---|
| Pipe | native |> (R 4.1+) |
%>% only when you need its placeholder/. features |
| Data frame | tibble |
data.frame defaults (but it's fine) |
| Wrangle | dplyr + tidyr |
hand-rolled [, subset, aggregate |
| Read CSV | readr::read_csv (prod), data.table::fread (speed) |
read.csv |
| Excel / Parquet / DB | readxl / arrow / DBI+dbplyr |
— |
| Strings / dates / factors | stringr / lubridate / forcats |
base grepl/POSIXlt/factor juggling |
| Plot | ggplot2 |
base graphics (fine for throwaway plots) |
| Iterate | purrr::map_* + across() |
sapply (type-unstable); lapply ok in package code |
| Big / fast | data.table (or dtplyr, arrow+duckdb) |
— |
| Model | base lm/glm + broom; tidymodels for CV/tuning |
caret |
| Time series | tsibble + fable |
forecast::auto.arima (maintenance-only) |
| Reports | Quarto (.qmd) |
R Markdown (still works) |
| Reproducibility | renv + Projects + here() |
setwd(), saving .RData |
The analysis workflow (and where each reference lives)
import → tidy → transform → visualize → model → communicate
- Import — get data in: import-io.md
- Tidy & transform — the dplyr/tidyr core: tidyverse-core.md
- Clean types — strings, dates, factors: strings-dates-factors.md
- Iterate — map over many things, list-columns: iteration-functional.md
- Visualize — ggplot2 + EDA: visualization.md
- Model — tests, lm/glm, broom, tidymodels: modeling-stats.md
- Scale up — when dplyr is too slow: data-table.md
- Time series — tsibble/fable, xts: time-series.md
- Ship it — projects, renv, Quarto, testing: workflow-tooling.md
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/r-packages.json 2.0 KB
- references/data-table.md 9.4 KB
- references/import-io.md 16 KB
- references/iteration-functional.md 12 KB
- references/modeling-stats.md 12 KB
- references/strings-dates-factors.md 13 KB
- references/tidyverse-core.md 12 KB
- references/time-series.md 9.4 KB
- references/visualization.md 13 KB
- references/workflow-tooling.md 14 KB
- scripts/check-r-facts.py 9.6 KB runs code
- tests/run.sh 6.5 KB runs code
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.
- 1mo ago First seen · 129 lines · 83 tokens per session scan A ddc23bb2a67c
r-ops is a skill published in the GitHub repository 0xDarkMatter/claude-mods (68 stars, last pushed 4d ago), licensed MIT. It adds 83 tokens to every session and 2,238 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
cpp-reinforcement-learning
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: Implementing RL algorithms in C++ for performance-critical applications Building production RL systems with libtorch Creating replay buffers and experience storage Optimizing RL training with GPU acceleration…
pytorch-dataset-class-implementations
Guidelines and instructions for PyTorch Dataset class implementations.
pytorch-loss-functions-configurations
Guidelines and instructions for PyTorch loss functions configurations.
pytorch-model-layers-definitions
Guidelines and instructions for PyTorch model layers definitions.
pytorch-model-save-load-helpers
Guidelines and instructions for PyTorch model save load helpers.
pytorch-optimizer-parameters-mapping
Guidelines and instructions for PyTorch optimizer parameters mapping.