TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Detect long-term trends in time series data using parametric and non-parametric methods.
Install Trend Analysis
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench trend-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .claude/skills/trend-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .claude/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench trend-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .agents/skills/trend-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .agents/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench trend-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .cursor/skills/trend-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .cursor/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/lake-warming-attribution/environment/skills/trend-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench trend-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .gemini/skills/trend-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .gemini/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench trend-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .github/skills/trend-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .github/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill trend-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench trend-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/trend-analysis .opencode/skills/trend-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "trend-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/trend-analysis into .opencode/skills/trend-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trend-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
trend-analysis- GitHub stars
- 1.8k
- Token cost
- ~697 tokens
- SKILL.md length
- 211 words
- Files
- 1
- Skills in repo
- 178
- Repo updated
- First seen
- Licence
- MIT
At a glance
Detect long-term trends in time series data using parametric and non-parametric methods.
- Determining if a variable shows statistically significant increase
- SKILL.md covers Overview, Parametric Method: Linear…, Non-Parametric Method: Sen's… and Significance Levels, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Decrease over time
What it does
Trend Analysis is an agent skill from benchflow-ai/skillsbench. 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.
Its SKILL.md is about 700 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is MIT.
When your agent uses it
- Determining if a variable shows statistically significant increase
- Decrease over time
Example prompts
- “/trend-analysis”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Trend Analysis loads about 697 tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 211 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its MIT licence (© benchflow-ai). 211 words, ~697 tokens.
Download SKILL.mdSave it as
.claude/skills/trend-analysis/SKILL.md (or your agent's skills folder).- name
- trend-analysis
- description
- 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.
- license
- MIT
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.
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.
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 |
|---|---|---|
| Linear Regression | Easy to interpret, gives R² | Sensitive to outliers |
| Sen's Slope | Robust to outliers, no normality assumption | Slightly less statistical power |
Significance Levels
| p-value | Interpretation |
|---|---|
| p < 0.01 | Highly significant trend |
| p < 0.05 | Significant trend |
| p < 0.10 | Marginally significant |
| p >= 0.10 | No significant trend |
Example: Annual Precipitation Trend
import pandas as pd
import pymannkendall as mk
# Load annual precipitation data
df = pd.read_csv('precipitation.csv')
precip = df['Precipitation'].values
# Run Mann-Kendall test
result = mk.original_test(precip)
print(f"Sen's slope: {result.slope:.2f} mm/year")
print(f"p-value: {result.p:.2f}")
print(f"Trend: {result.trend}")Common Issues
| Issue | Cause | Solution |
|---|---|---|
| p-value = NaN | Too few data points | Need at least 8-10 years |
| Conflicting results | Methods have different assumptions | Trust Sen's slope for environmental data |
| Slope near zero but significant | Large sample size | Check practical significance |
Best Practices
- Use at least 10 data points for reliable results
- Prefer Sen's slope for environmental time series
- Report both slope magnitude and p-value
- Round results to 2 decimal places
© benchflow-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/lake-warming-attribution/environment/skills/trend-analysis of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Trend Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Trend Analysis this skillbenchflow-ai/skillsbench | 1.8k | — | ~697 | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.6k | 6 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Alphaear Predictorninehills/skills | 281 | 2 repos | ~531 | Automated safety check: Pass | None | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 |
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Timesfm Forecasting
zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
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Market prediction skill using Kronos. An agent skill from ninehills/skills.
Find Hypertable Candidates
timescale/pg-aiguide
A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.
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Related
Categories
Questions about Trend Analysis
What does Trend Analysis do?
Detect long-term trends in time series data using parametric and non-parametric methods. Trend Analysis is an agent skill from benchflow-ai/skillsbench. Detect long-term trends in time series data using parametric and non-parametric methods.
When should I use Trend Analysis?
Trend Analysis fits situations like: determining if a variable shows statistically significant increase; decrease over time.
How do I install Trend Analysis in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill trend-analysis -a claude-code`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/trend-analysis in benchflow-ai/skillsbench) into .claude/skills/trend-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Trend Analysis in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill trend-analysis -a codex`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/trend-analysis in benchflow-ai/skillsbench) into .agents/skills/trend-analysis in your project. Codex loads it when a task matches its description.
Can I use Trend Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill trend-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trend-analysis, .gemini/skills/trend-analysis, .github/skills/trend-analysis and .opencode/skills/trend-analysis in your project.
What does Trend Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Trend Analysis is instructions for the agent only. Our summary lists: Python 3.
Does Trend Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Trend Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Trend Analysis use?
Trend Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Trend Analysis use?
About 697 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Trend Analysis?
Skills that share tags, products or a category with Trend Analysis: TimesFM Forecasting (google-research/timesfm, 34k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.6k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Alphaear Predictor (ninehills/skills, 281 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Trend Analysis?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.