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3GPP release structure, versioning, TSG rounds, and release freeze concept. Use when understanding 3GPP release numbering, TSG round schedules, or finding specifications in releases.
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| name | 3gpp-releases |
| description | 3GPP release structure, versioning, TSG rounds, and release freeze concept. Use when understanding 3GPP release numbering, TSG round schedules, or finding specifications in releases. |
Official Release Timeline: https://www.3gpp.org/specifications-technologies/releases
3GPP specifications are published four times a year following quarterly Technical Specification Group (TSG) plenary meetings. Each Release represents a set of frozen technical specifications.
3GPP uses a version numbering scheme:
21.456 (Series number + spec number)Examples:
A TSG round is a quarterly plenary meeting where specifications are made available. Each TSG round produces:
Four TSG plenaries per year:
Example 2025 Schedule:
https://www.3gpp.org/ftp/Specs/<YYYY>-<MM>/
TSG Round Examples:
https://www.3gpp.org/ftp/Specs/2025-12/https://www.3gpp.org/ftp/Specs/2025-12/SpecslistexTSG/TSG110.htmhttps://www.3gpp.org/ftp/Specs/2025-12/SpecslistexTSG/TSG110.htmOlder releases that are "closed" - no further Change Requests accepted.
# FTP directory for Release 18
base_url = "https://www.3gpp.org/ftp/Specs/2018-12/"
# List all spec files
spec_list = requests.get(base_url).text.split("\n")
TS numbers follow pattern: <series>.<spec>, where <series> is 21, 22, 23, etc.
Examples:
3gpp-working-groups - For understanding which TSGs produce which specs3gpp-specifications - For spec file format and numbering3gpp-change-request - For modifications to specs in releasesCreate, style, export, and validate publication-quality scientific and technical figures. xy is the preferred default (matplotlib.pyplot-compatible for easy migration); matplotlib/seaborn/plotly also supported. Use when the user asks for a "publication figure", "paper figure", "journal-ready plot", "scientific visualization", "SVG/PDF/TIFF export", "colorblind-safe palette", "multi-panel layout", "error bars / significance markers", or works with figure scripts that import matplotlib, seaborn, plotly, or xy.
Guide for creating, validating, improving, and benchmarking skills. Use when users want to create a skill from scratch, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Generate comprehensive database schemas with proper relations, migrations, and ORM/ODM models for PostgreSQL, MongoDB, and SQLite. Use when creating database schemas that integrate with FastAPI applications, including SQLAlchemy models for SQL databases, PyMongo/ODMantic models for MongoDB, Alembic migrations, and proper relationship definitions.