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.
Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data.
Install Lomb Scargle Periodogram
$ npx skills add benchflow-ai/skillsbench --skill lomb-scargle-periodogram -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench lomb-scargle-periodogram --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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .claude/skills/lomb-scargle-periodogram && 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 "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .claude/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogramType 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 lomb-scargle-periodogram -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench lomb-scargle-periodogram --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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .agents/skills/lomb-scargle-periodogram && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .agents/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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 lomb-scargle-periodogram -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench lomb-scargle-periodogram --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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .cursor/skills/lomb-scargle-periodogram && 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 "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .cursor/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram--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 lomb-scargle-periodogram -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench lomb-scargle-periodogram --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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .gemini/skills/lomb-scargle-periodogram && 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 "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .gemini/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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 lomb-scargle-periodogramInstalls 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 lomb-scargle-periodogram -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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .github/skills/lomb-scargle-periodogram && 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 "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .github/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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 lomb-scargle-periodogram -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 lomb-scargle-periodogram --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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram .opencode/skills/lomb-scargle-periodogram && 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 "lomb-scargle-periodogram" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram into .opencode/skills/lomb-scargle-periodogram/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lomb-scargle-periodogram", 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
lomb-scargle-periodogram- GitHub stars
- 1.8k
- Token cost
- ~883 tokens
- SKILL.md length
- 249 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data.
- Works in 3 steps: Single strong peak: Likely the true period → Harmonics: Peaks at period/2, period*2… → Aliases: Check if period*2 or period/2…
- Analyzing light curves
- SKILL.md covers Overview, Basic Usage with Lightkurve, Plotting Periodograms and Period Range Selection, plus 5 more sections
- Calls pip
What it does
Lomb Scargle Periodogram is an agent skill from benchflow-ai/skillsbench. Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.
Its SKILL.md is about 880 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 Apache-2.0.
When your agent uses it
- Analyzing light curves
- Radial velocity data
- Any astronomical time series to detect periodic variations
Example prompts
- “/lomb-scargle-periodogram”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Single strong peak: Likely the true period
- Harmonics: Peaks at period/2, period*2 suggest the fundamental period
- Aliases: Check if period*2 or period/2 also show signals
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
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
lightkurve.github.iodocs.lightkurve.org
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
Lomb Scargle Periodogram loads about 883 tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 249 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 Apache-2.0 licence (© benchflow-ai). 249 words, ~883 tokens.
Download SKILL.mdSave it as
.claude/skills/lomb-scargle-periodogram/SKILL.md (or your agent's skills folder).- name
- lomb-scargle-periodogram
- description
- Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.
Lomb-Scargle Periodogram
The Lomb-Scargle periodogram is the standard tool for finding periods in unevenly sampled astronomical time series data. It's particularly useful for detecting periodic signals in light curves from space missions like Kepler, K2, and TESS.
Overview
The Lomb-Scargle periodogram extends the classical periodogram to handle unevenly sampled data, which is common in astronomy due to observing constraints, data gaps, and variable cadences.
Basic Usage with Lightkurve
import lightkurve as lk
import numpy as np
# Create a light curve object
lc = lk.LightCurve(time=time, flux=flux, flux_err=error)
# Create periodogram (specify maximum period to search)
pg = lc.to_periodogram(maximum_period=15) # Search up to 15 days
# Find strongest period
strongest_period = pg.period_at_max_power
max_power = pg.max_power
print(f"Strongest period: {strongest_period:.5f} days")
print(f"Power: {max_power:.5f}")Plotting Periodograms
import matplotlib.pyplot as plt
pg.plot(view='period') # View vs period (not frequency)
plt.xlabel('Period [days]')
plt.ylabel('Power')
plt.show()Important: Use view='period' to see periods directly, not frequencies. The default view='frequency' shows frequency (1/period).
Period Range Selection
Choose appropriate period ranges based on your science case:
- Stellar rotation: 0.1 - 100 days
- Exoplanet transits: 0.5 - 50 days (most common)
- Eclipsing binaries: 0.1 - 100 days
- Stellar pulsations: 0.001 - 1 day
# Search specific period range
pg = lc.to_periodogram(minimum_period=2.0, maximum_period=7.0)Interpreting Results
Power Significance
Higher power indicates stronger periodic signal, but be cautious:
- High power: Likely real periodic signal
- Multiple peaks: Could indicate harmonics (period/2, period*2)
- Aliasing: Very short periods may be aliases of longer periods
Common Patterns
- Single strong peak: Likely the true period
- Harmonics: Peaks at period/2, period*2 suggest the fundamental period
- Aliases: Check if period*2 or period/2 also show signals
Model Fitting
Once you find a period, you can fit a model:
# Get the frequency at maximum power
frequency = pg.frequency_at_max_power
# Create a model light curve
model = pg.model(time=lc.time, frequency=frequency)
# Plot data and model
import matplotlib.pyplot as plt
lc.plot(label='Data')
model.plot(label='Model')
plt.legend()
plt.show()Dependencies
pip install lightkurve numpy matplotlibReferences
When to Use This vs. Other Methods
- Lomb-Scargle: General periodic signals (rotation, pulsation, eclipsing binaries)
- Transit Least Squares (TLS): Specifically for exoplanet transits (more sensitive)
- Box Least Squares (BLS): Alternative transit detection method
For exoplanet detection, consider using TLS after Lomb-Scargle for initial period search.
© benchflow-ai, Apache-2.0. 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/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Lomb Scargle Periodogram 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 |
|---|---|---|---|---|---|---|
| Lomb Scargle Periodogram this skillbenchflow-ai/skillsbench | 1.8k | — | ~883 | Automated safety check: Pass | Apache-2.0 | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.7k | 3 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Pensieve Searcharkohut/pensieve | 1.4k | — | ~8.2k | Automated safety check: Pass | Apache-2.0 |
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Questions about Lomb Scargle Periodogram
What does Lomb Scargle Periodogram do?
Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Lomb Scargle Periodogram is an agent skill from benchflow-ai/skillsbench. Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data.
When should I use Lomb Scargle Periodogram?
Lomb Scargle Periodogram fits situations like: analyzing light curves; radial velocity data; any astronomical time series to detect periodic variations.
How do I install Lomb Scargle Periodogram in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill lomb-scargle-periodogram -a claude-code`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram in benchflow-ai/skillsbench) into .claude/skills/lomb-scargle-periodogram in your project. Claude Code loads it when a task matches its description.
How do I install Lomb Scargle Periodogram in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill lomb-scargle-periodogram -a codex`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/lomb-scargle-periodogram in benchflow-ai/skillsbench) into .agents/skills/lomb-scargle-periodogram in your project. Codex loads it when a task matches its description.
Can I use Lomb Scargle Periodogram 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 lomb-scargle-periodogram -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lomb-scargle-periodogram, .gemini/skills/lomb-scargle-periodogram, .github/skills/lomb-scargle-periodogram and .opencode/skills/lomb-scargle-periodogram in your project.
What does Lomb Scargle Periodogram need to run?
Going by SKILL.md and its folder, Lomb Scargle Periodogram needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Lomb Scargle Periodogram access the network?
SKILL.md names 2 domains. As links in the text: lightkurve.github.io and docs.lightkurve.org. This is read from the text; nothing was executed.
Is Lomb Scargle Periodogram 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 Lomb Scargle Periodogram use?
Lomb Scargle Periodogram is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Lomb Scargle Periodogram use?
About 883 tokens (SKILL.md is roughly 3.5k 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 Lomb Scargle Periodogram?
Skills that share tags, products or a category with Lomb Scargle Periodogram: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Lomb Scargle Periodogram?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 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.