Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xuansenpa1/skillrevise --skill exoplanet-workflowsgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xuansenpa1/skillrevise/exoplanet-workflows)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/exoplanet-workflows"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/exoplanet-workflows/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/exoplanet-workflows"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/exoplanet-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.01526 |
| Opus 5 | $0.00023 | $0.00763 |
| Sonnet 5 | $0.00009 | $0.00305 |
| Haiku 4.5 | $0.00005 | $0.00153 |
Grade A, and why
exoplanet-workflows scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to exoplanet-workflows — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 204 lines · 45 tokens per session scan A 5ebc27240999
exoplanet-workflows is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 4d ago), licensed MIT. It adds 45 tokens to every session and 1,526 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to exoplanet-workflows, differing in 0 lines, and is treated as a copy.
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