Threat Detection
alirezarezvani/claude-skills
A skill your agent uses when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry.
General workflows and best practices for exoplanet detection and characterization from light curve data.
Apache-2.0Auto-check passed
Install Exoplanet Workflows
$ npx skills add benchflow-ai/skillsbench --skill exoplanet-workflows -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench exoplanet-workflows --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/exoplanet-workflows .claude/skills/exoplanet-workflows && 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 "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .claude/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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/exoplanet-workflowsType 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 exoplanet-workflows -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench exoplanet-workflows --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/exoplanet-workflows .agents/skills/exoplanet-workflows && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .agents/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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 exoplanet-workflows -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench exoplanet-workflows --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/exoplanet-workflows .cursor/skills/exoplanet-workflows && 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 "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .cursor/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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/exoplanet-workflows--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 exoplanet-workflows -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench exoplanet-workflows --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/exoplanet-workflows .gemini/skills/exoplanet-workflows && 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 "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .gemini/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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 exoplanet-workflowsInstalls 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 exoplanet-workflows -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/exoplanet-workflows .github/skills/exoplanet-workflows && 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 "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .github/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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 exoplanet-workflows -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 exoplanet-workflows --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/exoplanet-workflows .opencode/skills/exoplanet-workflows && 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 "exoplanet-workflows" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows into .opencode/skills/exoplanet-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exoplanet-workflows", 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
exoplanet-workflows- GitHub stars
- 1.8k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 737 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
General workflows and best practices for exoplanet detection and characterization from light curve data.
- Works in 5 steps: Data loading and quality control → Preprocessing to remove instrumental and… → Period search using appropriate algorithms → …
- Planning an exoplanet analysis pipeline
- SKILL.md covers Overview, Pipeline Design Principles, Choosing the Right Method and Signal Validation, plus 7 more sections
- Calls pip
What it does
Exoplanet Workflows is an agent skill from benchflow-ai/skillsbench. General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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
- Planning an exoplanet analysis pipeline
- Understanding when to use different methods
- Troubleshooting detection issues
Example prompts
- “/exoplanet-workflows”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Data loading and quality control
- Preprocessing to remove instrumental and stellar noise
- Period search using appropriate algorithms
- Signal validation and characterization
- Parameter estimation
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):
github.comlightkurve.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
Exoplanet Workflows loads about 1.6k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 737 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). 737 words, ~1,591 tokens.
Download SKILL.mdSave it as
.claude/skills/exoplanet-workflows/SKILL.md (or your agent's skills folder).- name
- exoplanet-workflows
- description
- General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues.
Exoplanet Detection Workflows
This skill provides general guidance on exoplanet detection workflows, helping you choose the right approach for your data and goals.
Overview
Exoplanet detection from light curves typically involves:
- Data loading and quality control
- Preprocessing to remove instrumental and stellar noise
- Period search using appropriate algorithms
- Signal validation and characterization
- Parameter estimation
Pipeline Design Principles
Key Stages
- Data Loading: Understand your data format, columns, time system
- Quality Control: Filter bad data points using quality flags
- Preprocessing: Remove noise while preserving planetary signals
- Period Search: Choose appropriate algorithm for signal type
- Validation: Verify candidate is real, not artifact
- Refinement: Improve period precision if candidate is strong
Critical Decisions
What to preprocess?
- Remove outliers? Yes, but not too aggressively
- Remove trends? Yes, stellar rotation masks transits
- How much? Balance noise removal vs. signal preservation
Which period search algorithm?
- TLS: Best for transit-shaped signals (box-like dips)
- Lomb-Scargle: Good for any periodic signal, fast exploration
- BLS: Alternative to TLS, built into Astropy
What period range to search?
- Consider target star type and expected planet types
- Hot Jupiters: short periods (0.5-10 days)
- Habitable zone: longer periods (depends on star)
- Balance: wider range = more complete, but slower
When to refine?
- After finding promising candidate
- Narrow search around candidate period
- Improves precision for final measurement
Choosing the Right Method
Transit Least Squares (TLS)
Use when:
- Searching for transiting exoplanets
- Signal has transit-like shape (box-shaped dips)
- You have flux uncertainties
Advantages:
- Most sensitive for transits
- Handles grazing transits
- Provides transit parameters
Disadvantages:
- Slower than Lomb-Scargle
- Only detects transits (not RV planets, eclipsing binaries with non-box shapes)
Lomb-Scargle Periodogram
Use when:
- Exploring data for any periodic signal
- Detecting stellar rotation
- Finding pulsation periods
- Quick period search
Advantages:
- Fast
- Works for any periodic signal
- Good for initial exploration
Disadvantages:
- Less sensitive to shallow transits
- May confuse harmonics with true period
Box Least Squares (BLS)
Use when:
- Alternative to TLS for transits
- Available in astropy
Note: TLS generally performs better than BLS for exoplanet detection.
Signal Validation
Strong Candidate (TLS)
- SDE > 9: Very strong candidate
- SDE > 6: Strong candidate
- SNR > 7: Reliable signal
Warning Signs
- Low SDE (<6): Weak signal, may be false positive
- Period exactly half/double expected: Check for aliasing
- High odd-even mismatch: May not be planetary transit
How to Validate
- Signal strength metrics: Check SDE, SNR against thresholds
- Visual inspection: Phase-fold data at candidate period
- Odd-even consistency: Do odd and even transits have same depth?
- Multiple transits: More transits = more confidence
Multi-Planet Systems
Some systems have multiple transiting planets. Strategy:
- Find first candidate
- Mask out first planet's transits
- Search remaining data for additional periods
- Repeat until no more significant signals
See Transit Least Squares documentation for transit_mask function.
Show full SKILL.md (281 more words)Show less
Common Issues and Solutions
Issue: No significant detection (low SDE)
Solutions:
- Check preprocessing - may be removing signal
- Try less aggressive outlier removal
- Check for data gaps during transits
- Signal may be too shallow for detection
Issue: Period is 2x or 0.5x expected
Causes:
- Period aliasing from data gaps
- Missing alternate transits
Solutions:
- Check both periods manually
- Look at phase-folded light curves
- Check if one shows odd-even mismatch
Issue: flux_err required error
Solution: TLS requires flux uncertainties as the third argument - they're not optional!
Issue: Results vary with preprocessing
Diagnosis:
- Compare results with different preprocessing
- Plot each preprocessing step
- Ensure you're not over-smoothing
Expected Transit Depths
For context:
- Hot Jupiters: 0.01-0.03 (1-3% dip)
- Super-Earths: 0.001-0.003 (0.1-0.3% dip)
- Earth-sized: 0.0001-0.001 (0.01-0.1% dip)
Detection difficulty increases dramatically for smaller planets.
Period Range Guidelines
Based on target characteristics:
- Hot Jupiters: 0.5-10 days
- Warm planets: 10-100 days
- Habitable zone:
- Sun-like star: 200-400 days
- M-dwarf: 10-50 days
Adjust search ranges based on mission duration and expected planet types.
Best Practices
- Always include flux uncertainties - critical for proper weighting
- Visualize each preprocessing step - ensure you're improving data quality
- Check quality flags - verify convention (flag=0 may mean good OR bad)
- Use appropriate sigma - 3 for initial outliers, 5 after flattening
- Refine promising candidates - narrow period search for precision
- Validate detections - check SDE, SNR, phase-folded plots
- Consider data gaps - may cause period aliasing
- Document your workflow - reproducibility is key
References
Official Documentation
Key Papers
- Hippke & Heller (2019) - Transit Least Squares paper
- Kovács et al. (2002) - BLS algorithm
Lightkurve Tutorial Sections
- Section 3.1: Identifying transiting exoplanet signals
- Section 2.3: Removing instrumental noise
- Section 3.2: Creating periodograms
Dependencies
pip install lightkurve transitleastsquares numpy matplotlib scipy© 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/exoplanet-workflows of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Exoplanet Workflows 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 |
|---|---|---|---|---|---|---|
| Exoplanet Workflows this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Threat Detectionalirezarezvani/claude-skills | 28k | — | ~3.5k | Automated safety check: Pass | MIT | |
| Resemble Detectgithub/awesome-copilot | 40k | 3 repos | ~4.1k | Automated safety check: Pass | Apache-2.0 | |
| Pii Detectruvnet/ruflo | 74k | — | ~350 | Automated safety check: Notes | MIT | |
| Detecting Dnp3 Protocol Anomaliesmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Detecting Attacks On Scada Systemsmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~6.9k | Automated safety check: Pass | Apache-2.0 |
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Questions about Exoplanet Workflows
What does Exoplanet Workflows do?
General workflows and best practices for exoplanet detection and characterization from light curve data. Exoplanet Workflows is an agent skill from benchflow-ai/skillsbench. General workflows and best practices for exoplanet detection and characterization from light curve data.
When should I use Exoplanet Workflows?
Exoplanet Workflows fits situations like: planning an exoplanet analysis pipeline; understanding when to use different methods; troubleshooting detection issues.
How do I install Exoplanet Workflows in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill exoplanet-workflows -a claude-code`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows in benchflow-ai/skillsbench) into .claude/skills/exoplanet-workflows in your project. Claude Code loads it when a task matches its description.
How do I install Exoplanet Workflows in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill exoplanet-workflows -a codex`. Or copy the skill folder (tasks/exoplanet-detection-period/environment/skills/exoplanet-workflows in benchflow-ai/skillsbench) into .agents/skills/exoplanet-workflows in your project. Codex loads it when a task matches its description.
Can I use Exoplanet Workflows 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 exoplanet-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exoplanet-workflows, .gemini/skills/exoplanet-workflows, .github/skills/exoplanet-workflows and .opencode/skills/exoplanet-workflows in your project.
What does Exoplanet Workflows need to run?
Going by SKILL.md and its folder, Exoplanet Workflows needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Exoplanet Workflows access the network?
SKILL.md names 2 domains. As links in the text: github.com and lightkurve.github.io. This is read from the text; nothing was executed.
Is Exoplanet Workflows 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 Exoplanet Workflows use?
Exoplanet Workflows 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 Exoplanet Workflows use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 Exoplanet Workflows?
Skills that share tags, products or a category with Exoplanet Workflows: Threat Detection (alirezarezvani/claude-skills, 28k stars), Resemble Detect (github/awesome-copilot, 40k stars), Pii Detect (ruvnet/ruflo, 74k stars) and Detecting Dnp3 Protocol Anomalies (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Exoplanet Workflows?
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.