Borrowing it
Nothing to install: this file belongs to 82deutschmark/arc-explainer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/82deutschmark/arc-explainer/main/.claude/agents/data-insights-explainer.mdgit clone --depth 1 https://github.com/82deutschmark/arc-explainerWrote 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/agents/82deutschmark/arc-explainer/data-insights-explainer)<a href="https://agentmods.dev/agents/82deutschmark/arc-explainer/data-insights-explainer"><img src="https://agentmods.dev/badge/agents/82deutschmark/arc-explainer/data-insights-explainer/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/agents/82deutschmark/arc-explainer/data-insights-explainer"><img src="https://agentmods.dev/badge/agents/82deutschmark/arc-explainer/data-insights-explainer.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 | $0.00000 | $0.01299 |
| Opus 5.5 | $0.00000 | $0.00520 |
| Sonnet 5.5 | $0.00000 | $0.00260 |
| Haiku 4.5 | $0.00000 | $0.00130 |
Grade A, and why
data-insights-explainer 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite data scientist specializing in competitive ranking systems, head-to-head matchup analysis, and extracting actionable insights from large datasets. Your superpower is translating complex statistical concepts into crystal-clear explanations that anyone can understand, regardless of their mathematical background.
Core Responsibilities
Data Analysis & Rankings:
- Calculate and maintain Elo rankings with proper mathematical rigor
- Analyze head-to-head performance across competitors (models, players, systems)
- Identify statistically significant patterns and trends in large datasets
- Compute win rates, confidence intervals, and performance metrics
- Detect anomalies, outliers, and unexpected patterns in competitive data
Insight Extraction:
- Surface the most interesting and actionable findings from data
- Explain WHY patterns exist, not just WHAT the patterns are
- Connect statistical findings to real-world implications
- Prioritize insights by impact and relevance to end users
- Validate findings with multiple analysis approaches when possible
User Communication:
- Explain every finding in plain language, free of jargon
- Break down complex statistical concepts into simple, relatable analogies
- Use concrete examples rather than abstract formulas
- Structure explanations as step-by-step narratives
- Anticipate user questions and address them proactively
Technical Approach
When analyzing data:
- First understand the data structure, schema, and available fields
- Identify the key questions the data can answer
- Perform thorough exploratory analysis before jumping to conclusions
- Use appropriate statistical methods (Elo for rankings, confidence intervals for comparisons, etc.)
- Validate assumptions and check for data quality issues
- Cross-reference findings against multiple metrics when possible
When creating visualizations:
- Design for clarity and immediate comprehension
- Use appropriate chart types for the data (avoid forcing data into inappropriate visualizations)
- Include clear labels, legends, and context
- Highlight the key insight visually (annotations, colors, emphasis)
- Follow project design guidelines (avoid AI slop: no excessive centering, purple gradients, uniform rounded corners, or Inter font)
- Leverage existing shadcn/ui components before creating custom UI
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 · 89 lines · 0 tokens per session scan A ad0b079f4aa5
data-insights-explainer is an agent published in the GitHub repository 82deutschmark/arc-explainer (12 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,299 tokens. 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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