trend-analysis SKILL.md skillA
trend-analysis from benchflow-ai/benchflow is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add benchflow-ai/benchflow/trend-analysis@git:20260610.e2a435fPins git:20260610.e2a435f in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- benchflow-ai/benchflow
- Path
tests/fixtures/skillsbench_slice/lake-warming-attribution/environment/skills/trend-analysis/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260610.e2a435f, committed Jun 10, 2026
- Size
- 2,788 bytes
- Repository stars
- 359
- License
- Apache-2.0
Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Trend Analysis Guide
## Overview
Trend analysis determines whether a time series shows a statistically significant long-term increase or decrease. This guide covers both parametric (linear regression) and non-parametric (Sen's slope) methods.
## Parametric Method: Linear Regression
Linear regression fits a straight line to the data and tests if the slope is significantly different from zero.
```python
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(years, values)
print(f"Slope: {slope:.2f} units/year")
print(f"p-value: {p_value:.2f}")
```
### Assumptions
- Linear relationship between time and variable
- Residuals are normally distributed
- Homoscedasticity (constant variance)
## Non-Parametric Method: Sen's Slope with Mann-Kendall Test
Sen's slope is robust to outliers and does not assume normality. Recommended for environmental data.
```python
import pymannkendall as mk
result = mk.original_test(values)
print(result.slope) # Sen's slope (rate of change per time unit)
print(result.p) # p-value for significance
print(result.trend) # 'increasing', 'decreasing', or 'no trend'
```
### Comparison
| Method | Pros | Cons |
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How to install
mdr add benchflow-ai/benchflow/trend-analysis@sha256:eec026677dd73c74The line at the top of this page pins the label git:20260610.e2a435f, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_3r5an3odcjcqha5s)
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Versions
The registry has recorded 1 version of this file, committed on Jun 10, 2026.
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Source
benchflow-ai/benchflow · 359 stars · license Apache-2.0 · pushed 2026-10-30 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_3r5an3odcjcqha5s GET https://markdownregistry.com/api/v1/resolve?ref=benchflow-ai/benchflow/trend-analysis GET https://markdownregistry.com/api/v1/blob/eec026677dd73c7407170d01fc2358ba10dacfd4b8b3b9bf669b8f922491514f
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