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 justanesta/claude-code-resources --skill optimizing-rgit clone --depth 1 https://github.com/justanesta/claude-code-resourcesWrote 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/justanesta/claude-code-resources/optimizing-r)<a href="https://agentmods.dev/skills/justanesta/claude-code-resources/optimizing-r"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/optimizing-r/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/justanesta/claude-code-resources/optimizing-r"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/optimizing-r.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00082 | $0.00996 |
| Opus 5.5 | $0.00033 | $0.00398 |
| Sonnet 5.5 | $0.00016 | $0.00199 |
| Haiku 4.5 | $0.00008 | $0.00100 |
Grade A, and why
optimizing-r 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 2mo 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimizing R
This skill covers profiling, benchmarking, parallelization, and performance best practices for R.
Core Principle
Profile before optimizing - Use profvis and bench to identify real bottlenecks. Write readable code first, optimize only when necessary.
Profiling Tools Decision Matrix
| Tool | Use When | Don't Use When | What It Shows |
|---|---|---|---|
profvis |
Complex code, unknown bottlenecks | Simple functions, known issues | Time per line, call stack |
bench::mark() |
Comparing alternatives | Single approach | Relative performance, memory |
system.time() |
Quick checks | Detailed analysis | Total runtime only |
Rprof() |
Base R only environments | When profvis available | Raw profiling data |
Performance Workflow
- Profile first - Find the actual bottlenecks
- Focus on the slowest parts - 80/20 rule
- Benchmark alternatives - For hot spots only
- Consider tool trade-offs - Based on bottleneck type
See profiling-workflow.md for the complete workflow.
When Each Tool Helps vs Hurts
Parallel Processing (in_parallel())
Helps when:
- CPU-intensive computations
- Embarrassingly parallel problems
- Large datasets with independent operations
- I/O bound operations (file reading, API calls)
Hurts when:
- Simple, fast operations (overhead > benefit)
- Memory-intensive operations (may cause thrashing)
- Operations requiring shared state
- Small datasets
See parallel-examples.md for decision points.
Data Backend Selection
| Backend | Use When |
|---|---|
| data.table | Very large datasets (>1GB), complex grouping, maximum performance critical |
| dplyr | Readability priority, complex joins/window functions, moderate data (<100MB) |
| base R | No dependencies allowed, simple operations, teaching/learning |
See backend-selection.md for guidance.
What ships with it
6 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.
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.
- 2mo ago First seen · 109 lines · 82 tokens per session scan A ccd1e3699c03
optimizing-r is a skill published in the GitHub repository justanesta/claude-code-resources (2 stars, last pushed 6mo ago), licensed MIT. It adds 82 tokens to every session and 996 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 2026-08-31.
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