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 agentmods add skills/vixues/leagent/data-analyzernpx skills add vixues/LeAgent --skill data-analyzergit clone --depth 1 https://github.com/vixues/LeAgentWhat 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 | $0.00039 | $0.00390 |
| Opus 5 | $0.00019 | $0.00195 |
| Sonnet 5 | $0.00008 | $0.00078 |
| Haiku 4.5 | $0.00004 | $0.00039 |
Grade A, and why
data-analyzer 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 2d 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.
What it actually says
Data Analysis
You are assisting with data analysis tasks. Follow these guidelines.
Analysis Workflow
- Understand the data: identify columns, types, ranges, and any quality issues.
- Clean the data: handle missing values, outliers, and format inconsistencies.
- Analyze: compute relevant statistics (counts, sums, averages, distributions).
- Compare: when multiple datasets or time periods exist, provide comparative analysis.
- Summarize: present findings clearly with key metrics highlighted.
Statistical Methods
- Use descriptive statistics (mean, median, mode, std dev) as a baseline.
- Identify trends and patterns — year-over-year, month-over-month, category breakdowns.
- Flag outliers and anomalies with context about their potential significance.
- For comparisons, compute both absolute and percentage differences.
Output Formats
- Summary: Concise paragraph with key findings and numbers.
- Table: Structured tabular format for detailed breakdowns.
- Report: Sectioned report with executive summary, methodology, findings, and recommendations.
Best Practices
- Always state the sample size and time range of the data being analyzed.
- Round numbers appropriately for readability (2 decimal places for percentages).
- When making comparisons, ensure the baseline and comparison period are clear.
- Distinguish between correlation and causation in findings.
- Provide actionable recommendations when the analysis supports them.
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.
- 2d ago First seen · 44 lines · 39 tokens per session scan A 9bdb480247eb
data-analyzer is a skill published in the GitHub repository vixues/LeAgent (216 stars, last pushed 21d ago), licensed Apache-2.0. It adds 39 tokens to every session and 390 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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