Statistical Analysis
majiayu000/claude-skill-registry
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing.
Preprocessing and cleaning techniques for astronomical light curves.
Install Light Curve Preprocessing
$ npx skills add benchflow-ai/skillsbench --skill light-curve-preprocessing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench light-curve-preprocessing --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/light-curve-preprocessing .claude/skills/light-curve-preprocessing && 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 "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .claude/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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/light-curve-preprocessingType 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 light-curve-preprocessing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench light-curve-preprocessing --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/light-curve-preprocessing .agents/skills/light-curve-preprocessing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .agents/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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 light-curve-preprocessing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench light-curve-preprocessing --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/light-curve-preprocessing .cursor/skills/light-curve-preprocessing && 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 "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .cursor/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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/light-curve-preprocessing--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 light-curve-preprocessing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench light-curve-preprocessing --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/light-curve-preprocessing .gemini/skills/light-curve-preprocessing && 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 "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .gemini/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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 light-curve-preprocessingInstalls 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 light-curve-preprocessing -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/light-curve-preprocessing .github/skills/light-curve-preprocessing && 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 "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .github/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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 light-curve-preprocessing -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 light-curve-preprocessing --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/light-curve-preprocessing .opencode/skills/light-curve-preprocessing && 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 "light-curve-preprocessing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing into .opencode/skills/light-curve-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-curve-preprocessing", 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
light-curve-preprocessing- GitHub stars
- 1.8k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 399 words
- Files
- 1
- Skills in repo
- 180
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Preprocessing and cleaning techniques for astronomical light curves.
- Works in 4 steps: Remove outliers → Remove long-term trends → Handle data quality flags → …
- Preparing light curve data for period analysis
- SKILL.md covers Overview, Outlier Removal, Removing Long-Term Trends and Handling Data Quality Flags, plus 6 more sections
- Calls pip
What it does
Light Curve Preprocessing is an agent skill from benchflow-ai/skillsbench. Preprocessing and cleaning techniques for astronomical light curves. Use when preparing light curve data for period analysis, including outlier removal, trend removal, flattening, and handling data quality flags. Works with lightkurve and general time series data.
Its SKILL.md is about 1.4k 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 Data cleaning and 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
- Preparing light curve data for period analysis
- Including outlier removal
- Handling data quality flags
Example prompts
- “/light-curve-preprocessing”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Remove outliers
- Remove long-term trends
- Handle data quality flags
- Remove stellar variability (optional)
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.io
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
Light Curve Preprocessing loads about 1.4k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 399 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). 399 words, ~1,378 tokens.
Download SKILL.mdSave it as
.claude/skills/light-curve-preprocessing/SKILL.md (or your agent's skills folder).- name
- light-curve-preprocessing
- description
- Preprocessing and cleaning techniques for astronomical light curves. Use when preparing light curve data for period analysis, including outlier removal, trend removal, flattening, and handling data quality flags. Works with lightkurve and general time series data.
Light Curve Preprocessing
Preprocessing is essential before period analysis. Raw light curves often contain outliers, long-term trends, and instrumental effects that can mask or create false periodic signals.
Overview
Common preprocessing steps:
- Remove outliers
- Remove long-term trends
- Handle data quality flags
- Remove stellar variability (optional)
Outlier Removal
Using Lightkurve
import lightkurve as lk
# Remove outliers using sigma clipping
lc_clean, mask = lc.remove_outliers(sigma=3, return_mask=True)
outliers = lc[mask] # Points that were removed
# Common sigma values:
# sigma=3: Standard (removes ~0.3% of data)
# sigma=5: Conservative (removes fewer points)
# sigma=2: Aggressive (removes more points)Manual Outlier Removal
import numpy as np
# Calculate median and standard deviation
median = np.median(flux)
std = np.std(flux)
# Remove points beyond 3 sigma
good = np.abs(flux - median) < 3 * std
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]Removing Long-Term Trends
Flattening with Lightkurve
# Flatten to remove low-frequency variability
# window_length: number of cadences to use for smoothing
lc_flat = lc_clean.flatten(window_length=500)
# Common window lengths:
# 100-200: Remove short-term trends
# 300-500: Remove medium-term trends (typical for TESS)
# 500-1000: Remove long-term trendsThe flatten() method uses a Savitzky-Golay filter to remove trends while preserving transit signals.
Iterative Sine Fitting
For removing high-frequency stellar variability (rotation, pulsation):
def sine_fitting(lc):
"""Remove dominant periodic signal by fitting sine wave."""
pg = lc.to_periodogram()
model = pg.model(time=lc.time, frequency=pg.frequency_at_max_power)
lc_new = lc.copy()
lc_new.flux = lc_new.flux / model.flux
return lc_new, model
# Iterate multiple times to remove multiple periodic components
lc_processed = lc_clean.copy()
for i in range(50): # Number of iterations
lc_processed, model = sine_fitting(lc_processed)Warning: This removes periodic signals, so use carefully if you're searching for periodic transits.
Handling Data Quality Flags
IMPORTANT: Quality flag conventions vary by data source!
Standard TESS format
# For standard TESS files (flag=0 is GOOD):
good = flag == 0
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]Alternative formats
# For some exported files (flag=0 is BAD):
good = flag != 0
time_clean = time[good]
flux_clean = flux[good]
error_clean = error[good]Always verify your data format! Check which approach gives cleaner results.
Preprocessing Pipeline Considerations
When building a preprocessing pipeline for exoplanet detection:
Key Steps (Order Matters!)
- Quality filtering: Apply data quality flags first
- Outlier removal: Remove bad data points (flares, cosmic rays)
- Trend removal: Remove long-term variations (stellar rotation, instrumental drift)
- Optional second pass: Additional outlier removal after detrending
Important Principles
- Always include flux_err: Critical for proper weighting in period search algorithms
- Preserve transit shapes: Use methods like
flatten()that preserve short-duration dips - Don't over-process: Too aggressive preprocessing can remove real signals
- Verify visually: Plot each step to ensure quality
Show full SKILL.md (178 more words)Show less
Parameter Selection
- Outlier removal sigma: Lower sigma (2-3) is aggressive, higher (5-7) is conservative
- Flattening window: Should be longer than transit duration but shorter than stellar rotation period
- When to do two passes: Remove obvious outliers before detrending, then remove residual outliers after
Preprocessing for Exoplanet Detection
For transit detection, be careful not to remove the transit signal:
- Remove outliers first: Use sigma=3 or sigma=5
- Flatten trends: Use window_length appropriate for your data
- Don't over-process: Too much smoothing can remove shallow transits
Visualizing Results
Always plot your light curve to verify preprocessing quality:
import matplotlib.pyplot as plt
# Use .plot() method on LightCurve objects
lc.plot()
plt.show()Best practice: Plot before and after each major step to ensure you're improving data quality, not removing real signals.
Dependencies
pip install lightkurve numpy matplotlibReferences
Best Practices
- Always check quality flags first: Remove bad data before processing
- Remove outliers before flattening: Outliers can affect trend removal
- Choose appropriate window length: Too short = doesn't remove trends, too long = removes transits
- Visualize each step: Make sure preprocessing improves the data
- Don't over-process: More preprocessing isn't always better
© 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/light-curve-preprocessing of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Light Curve Preprocessing 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 |
|---|---|---|---|---|---|---|
| Light Curve Preprocessing this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Statistical Analysismajiayu000/claude-skill-registry | 666 | 2 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Visual Skillsnpc-live/clawfirm | 156 | — | ~7.4k | Automated safety check: Pass | None | |
| Matlab Analyze Datamatlab/matlab-agentic-toolkit | 1.1k | — | ~3.2k | Automated safety check: Pass | Custom licence | |
| Cja Dimension Analysisadobe/skills | 195 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| ML Data Leakage Guardmajiayu000/claude-skill-registry | 666 | 1 repos | ~3.4k | Automated safety check: Pass | MIT |
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Categories
Questions about Light Curve Preprocessing
What does Light Curve Preprocessing do?
Preprocessing and cleaning techniques for astronomical light curves. Light Curve Preprocessing is an agent skill from benchflow-ai/skillsbench. Preprocessing and cleaning techniques for astronomical light curves.
When should I use Light Curve Preprocessing?
Light Curve Preprocessing fits situations like: preparing light curve data for period analysis; including outlier removal; handling data quality flags.
How do I install Light Curve Preprocessing in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill light-curve-preprocessing -a claude-code`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing in benchflow-ai/skillsbench) into .claude/skills/light-curve-preprocessing in your project. Claude Code loads it when a task matches its description.
How do I install Light Curve Preprocessing in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill light-curve-preprocessing -a codex`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/light-curve-preprocessing in benchflow-ai/skillsbench) into .agents/skills/light-curve-preprocessing in your project. Codex loads it when a task matches its description.
Can I use Light Curve Preprocessing 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 light-curve-preprocessing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/light-curve-preprocessing, .gemini/skills/light-curve-preprocessing, .github/skills/light-curve-preprocessing and .opencode/skills/light-curve-preprocessing in your project.
What does Light Curve Preprocessing need to run?
Going by SKILL.md and its folder, Light Curve Preprocessing needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Light Curve Preprocessing access the network?
SKILL.md names 1 domain. As links in the text: lightkurve.github.io. This is read from the text; nothing was executed.
Is Light Curve Preprocessing 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 Light Curve Preprocessing use?
Light Curve Preprocessing 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 Light Curve Preprocessing use?
About 1.4k tokens (SKILL.md is roughly 5.5k 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 Light Curve Preprocessing?
Skills that share tags, products or a category with Light Curve Preprocessing: Statistical Analysis (majiayu000/claude-skill-registry, 666 stars), Visual Skills (npc-live/clawfirm, 156 stars), Matlab Analyze Data (matlab/matlab-agentic-toolkit, 1.1k stars) and Cja Dimension Analysis (adobe/skills, 195 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Light Curve Preprocessing?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 180 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.