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/danielrosehill/claude-data-analyst-plugin/trend-analysisnpx skills add danielrosehill/Claude-Data-Analyst-plugin --skill trend-analysisgit clone --depth 1 https://github.com/danielrosehill/Claude-Data-Analyst-pluginWrote 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/danielrosehill/claude-data-analyst-plugin/trend-analysis)<a href="https://agentmods.dev/skills/danielrosehill/claude-data-analyst-plugin/trend-analysis"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-data-analyst-plugin/trend-analysis.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.00751 |
| Opus 5 | $0.00026 | $0.00376 |
| Sonnet 5 | $0.00010 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
trend-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 5d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trend Analysis
Identify the major trends in a dataset and summarise them in a narrative report.
Inputs
- Path to a dataset file or folder.
- Optional: time column name (auto-detected if a single date/datetime column exists).
- Optional: metric(s) of interest — numeric columns to focus trend analysis on. Default: all numerics.
- Optional: segment column — to produce per-segment trend breakdowns.
Recommended CLI tooling
duckdb— windowed SQL aggregations (time_bucket, moving averages, YoY).uv run --with pandas --with statsmodels python -c '...'— STL decomposition, Mann-Kendall trend test, seasonal detection.mlr(miller) — quick pivots and tallies on CSV without loading pandas.
Procedure
Step 1 — Temporal trends (if time column exists)
For each metric:
- Overall direction: fit linear regression on the metric vs. time. Report slope, R², and sign (up/down/flat). Supplement with Mann-Kendall test for monotonic trend (robust to outliers and non-linearity).
- Growth rate: first-to-last period % change, and CAGR if span > 1 year.
- Level shifts / change points: detect structural breaks (PELT or simple rolling mean comparison).
- Seasonality: if ≥2 seasonal cycles present, run STL decomposition and report dominant period (weekly, monthly, yearly).
- Recency: compare last period vs. trailing average — is the trend accelerating, decelerating, or reversing?
Step 2 — Compositional trends (categorical)
For each categorical column:
- Which categories are growing in share over time?
- Which are shrinking?
- Any new entrants appearing only in recent periods?
- Any categories that disappeared?
Step 3 — Segment trends
If a segment column is provided, repeat Step 1 within each segment and surface:
- Segments with the strongest growth / decline
- Segments diverging from the overall trend (Simpson's-paradox watchlist)
Step 4 — Non-temporal trends
If no time column exists, "trend" becomes distributional:
- Skew, concentration (top-N share, Gini), long-tail structure
- Dominant vs. rare categories
- Relationships between paired columns (lean on
correlation-analysisfor the heavy lifting; summarise headlines only)
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.
- 5d ago First seen · 67 lines · 52 tokens per session scan A 77674018ceb1
trend-analysis is a skill published in the GitHub repository danielrosehill/Claude-Data-Analyst-plugin (11 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 751 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.
Other skills, from other repositories
ppt-analysis
PPT (.pptx/.ppt) 全量解析。覆盖：所有 slide 文本/表格/图表提取、嵌入图片 caption、纯图片 slide 渲染识别、数据标签提取。.
sn-image-base
Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only.
sn-search-academic
用于学术调研、论文精读、相关工作梳理、百科知识查询和引用链追溯。.
outlier-detection-and-quality-assessment
执行全面的异常值检测与数据质量评估，利用 IQR 方法识别异常值并结合偏度、峰度分析数据分布特征，适用于非正态分布数据的预处理阶段。.
multi-sheet-reading-and-analysis
用于读取多工作表Excel文件，动态评估数据量以启用Parquet大文件优化，并执行正则清洗、分类汇总、线性拟合及生成带格式的图表与结果文件。.
excel-data-analysis-and-report-generation
从Excel提取多类型数据，并生成包含可视化图表与下载链接的综合分析报告。.