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 Boom5426/Nature-Paper-Skills --skill results-analysisgit clone --depth 1 https://github.com/Boom5426/Nature-Paper-SkillsWrote 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/boom5426/nature-paper-skills/results-analysis)<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/results-analysis"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/results-analysis/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/boom5426/nature-paper-skills/results-analysis"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/results-analysis.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 | $0.00034 | $0.01979 |
| Opus 5 | $0.00017 | $0.00989 |
| Sonnet 5 | $0.00007 | $0.00396 |
| Haiku 4.5 | $0.00003 | $0.00198 |
Grade A, and why
results-analysis 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 12d 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Results Analysis for ML/AI Research
A systematic experimental results analysis workflow connecting experimental data to paper writing.
Core Features
This skill provides three core capabilities:
- Experimental Data Analysis - Read and analyze experimental data in various formats
- Statistical Validation - Perform statistical significance tests and performance comparisons
- Paper Content Generation - Generate text and visualizations for the Results section
When to Use
Use this skill when you need to:
- Analyze experimental results (CSV, JSON, TensorBoard logs)
- Generate the Results section of a paper
- Compare performance across multiple models
- Perform statistical significance tests
- Create publication-quality visualizations
- Validate the reliability of experimental results
Workflow
Standard Analysis Pipeline
Data Loading → Data Validation → Statistical Analysis → Visualization → Writing → Quality Check
Step 1: Data Loading and Validation
Supported Data Formats:
- CSV files - Tabular data
- JSON files - Structured results
- TensorBoard logs - Training curves
- Python pickle - Complex objects
Data Validation Checks:
- Completeness check - Missing values, outliers
- Consistency check - Data format, units
- Reproducibility check - Random seeds, version info
Select appropriate tools for data loading and preliminary validation based on data format.
Step 2: Statistical Analysis
Basic Statistics:
- Mean
- Standard Deviation
- Standard Error
- Confidence Interval
Significance Tests:
- t-test - Two-group comparison
- ANOVA - Multi-group comparison
- Wilcoxon test - Non-parametric test
- Bonferroni correction - Multiple comparison correction
Select appropriate statistical tests based on data characteristics.
Key Principles:
- Report complete statistical information (mean ± std/SE)
- Specify the test method and significance level used
- Report p-values and effect sizes
- Consider multiple comparison issues
What ships with it
7 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.
- 12d ago First seen · 336 lines · 34 tokens per session scan A bac4e1dbde03
results-analysis is a skill published in the GitHub repository Boom5426/Nature-Paper-Skills (490 stars, last pushed 7d ago), licensed MIT. It adds 34 tokens to every session and 1,979 once invoked, about $0.0002 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 2026-08-30.
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