Tokens
Track, visualize, and compete on AI coding assistant token usage across Claude Code, Cursor, OpenCode, Codex, Gemini, Kimi, and Qwen. The Kardashev Scale for AI Devs.
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尚未偵測到更新。GitHub 發布版本後會顯示在這裡。
個人 GitHub 帳號下公開儲存庫的擁有者可以登入並驗證所有權,自行管理介紹,無需管理員核准。
登入並產生驗證檔案,在 30 分鐘內將其提交至此儲存庫的預設分支。其他分支或分叉儲存庫中的檔案無效。然後在下方檢查檔案,即可無需管理員核准直接管理專案。請保留檔案以維持管理權限。
如需登入,將前往登入頁面。登入後請回到此處,再次點擊所選的驗證按鈕。
在專案資料夾中執行 /nomorevibe verify,即可驗證所有權並自行管理介紹。
一分鐘完成驗證製作者的介紹僅有上面的一句話。 更多詳情請閱讀儲存庫 README。 儲存庫 README ↗
正在載入完整 README。
檢視完整 README ↗A static, browsable catalog of exoplanets (and brown-dwarf / free-floating planet candidates) discovered through KMTNet (Korea Microlensing Telescope Network) microlensing surveys — styled after the NASA Exoplanet Archive.
Open index.html in a browser (or serve the folder with any static file
server) — no build step required.
data/kmtnet_planets.json — the merged dataset the site reads (via
assets/data/planets.js, a JS-wrapped copy for file://-friendly
loading without CORS issues).scripts/parse_numbers.py — rebuilds the base catalog (name, ADS
publication link, title, publication year, Planet/BD/FFP classification)
from the source .numbers spreadsheet.scripts/merge_nasa.py — merges physical parameters (mass,
semi-major axis, host-star mass, distance, RA/Dec, discovery
telescope/facility, release date) from a NASA Exoplanet Archive CSV
export (PSCompPars table, filtered to discoverymethod = Microlensing)
by matching planet name.scripts/merge_exoplaneteu.py — tops up whatever fields are still
blank after the NASA merge from an
exoplanet.eu catalog export (all
detection_type = Microlensing rows), matching by name and alternate
names. Never overwrites a value the NASA merge already filled in.scripts/build_site_data.py — regenerates assets/data/planets.js
from data/kmtnet_planets.json after any of the above.pip install numbers-parser
python scripts/parse_numbers.py path/to/source.numbers
python scripts/merge_nasa.py data/raw/nasa_pscomppars.csv
python scripts/merge_exoplaneteu.py data/raw/exoplanet_eu_catalog.csv
python scripts/build_site_data.py
Entries not yet matched to either source show TBD for physical
parameters rather than being hidden.
parse_numbers.py separates the
meaningful classification text from bookkeeping noise.