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 LigphiDonk/Oh-my--paper --skill inno-experiment-analysisgit clone --depth 1 https://github.com/LigphiDonk/Oh-my--paperWrote 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/ligphidonk/oh-my--paper/inno-experiment-analysis)<a href="https://agentmods.dev/skills/ligphidonk/oh-my--paper/inno-experiment-analysis"><img src="https://agentmods.dev/badge/skills/ligphidonk/oh-my--paper/inno-experiment-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/ligphidonk/oh-my--paper/inno-experiment-analysis"><img src="https://agentmods.dev/badge/skills/ligphidonk/oh-my--paper/inno-experiment-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 | $0.00050 | $0.02476 |
| Opus 5 | $0.00025 | $0.01238 |
| Sonnet 5 | $0.00010 | $0.00495 |
| Haiku 4.5 | $0.00005 | $0.00248 |
Grade A, and why
inno-experiment-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 13d 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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
inno-experiment-analysis
Canonical Summary
This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime...
Trigger Rules
Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.
Resource Use Rules
- Read from
references/only when the current task needs the extra detail.
Execution Contract
- Resolve every relative path from this skill directory first.
- Prefer inspection before mutation when invoking bundled scripts.
- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.
Upstream Instructions
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
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.
- 13d ago First seen · 387 lines · 50 tokens per session scan A d4e51730a94f
inno-experiment-analysis is a skill published in the GitHub repository LigphiDonk/Oh-my--paper (724 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 2,476 once invoked, about $0.0003 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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Structured data analysis workflow from raw data to shareable insights.
data-analysis
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