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sqlalchemy

Python SQL toolkit and Object Relational Mapper (ORM). Use when working with databases in Python, defining models, building queries, managing sessions, or interacting with SQL databases using Python objects.

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Python SQL toolkit and Object Relational Mapper (ORM). Use when working with databases in Python, defining models, building queries, managing sessions, or interacting with SQL databases using Python objects.

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SQLAlchemy Skill

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that provides the full power and flexibility of SQL. It consists of two main components: Core (SQL Expression Language) and ORM (Object Relational Mapper).

When to Use This Skill

Use SQLAlchemy when:

  • Working with relational databases in Python
  • Defining database models as Python classes
  • Building SQL queries programmatically
  • Managing database transactions and sessions
  • Mapping Python objects to database tables
  • Need database-agnostic code that works across PostgreSQL, MySQL, SQLite, etc.

Installation

pip install sqlalchemy

# For async support
pip install sqlalchemy[asyncio]

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    SQLAlchemy ORM                           │
│  (Declarative Mapping, Session, Relationships, Unit of Work)│
├─────────────────────────────────────────────────────────────┤
│                   SQLAlchemy Core                           │
│     (SQL Expression Language, Engine, Connection Pool)      │
├─────────────────────────────────────────────────────────────┤
│                        DBAPI                                │
│         (psycopg2, pymysql, sqlite3, etc.)                  │
└─────────────────────────────────────────────────────────────┘

Engine and Connection

The Engine is the starting point for SQLAlchemy applications:

from sqlalchemy import create_engine

# SQLite (in-memory)
engine = create_engine("sqlite://", echo=True)

# SQLite (file-based)
engine = create_engine("sqlite:///mydatabase.db")

# PostgreSQL
engine = create_engine("postgresql+psycopg2://user:password@localhost/dbname")

# MySQL
engine = create_engine("mysql+pymysql://user:password@localhost/dbname")

# Connection pool settings
engine = create_engine(
    "postgresql+psycopg2://user:password@localhost/dbname",
    pool_size=5,           # Number of connections to keep open
    max_overflow=10,       # Additional connections allowed
    pool_timeout=30,       # Seconds to wait for connection
    pool_recycle=1800,     # Recycle connections after N seconds
)
Using Connections Directly (Core)
from sqlalchemy import text

with engine.connect() as conn:
    result = conn.execute(text("SELECT * FROM users WHERE id = :id"), {"id": 1})
    for row in result:
        print(row)

    # For write operations, commit explicitly
    conn.execute(text("INSERT INTO users (name) VALUES (:name)"), {"name": "Alice"})
    conn.commit()

ORM Declarative Mapping

from datetime import datetime
from typing import List, Optional

from sqlalchemy import ForeignKey, String, func
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship


class Base(DeclarativeBase):
    pass


class User(Base):
    __tablename__ = "user_account"

    # Primary key with auto-increment
    id: Mapped[int] = mapped_column(primary_key=True)

    # Required string column with max length
    name: Mapped[str] = mapped_column(String(30))

    # Optional column (nullable)
    fullname: Mapped[Optional[str]]

    # Column with default value
    created_at: Mapped[datetime] = mapped_column(default=func.now())

    # One-to-many relationship
    addresses: Mapped[List["Address"]] = relationship(
        back_populates="user",
        cascade="all, delete-orphan"
    )

    def __repr__(self) -> str:
        return f"User(id={self.id!r}, name={self.name!r})"


class Address(Base):
    __tablename__ = "address"

    id: Mapped[int] = mapped_column(primary_key=True)
    email_address: Mapped[str]
    user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))

    # Many-to-one relationship (back reference)
    user: Mapped["User"] = relationship(back_populates="addresses")

    def __repr__(self) -> str:
        return f"Address(id={self.id!r}, email_address={self.email_address!r})"
Type Annotation Guide
Python TypeSQL TypeNullable
Mapped[int]INTEGERNOT NULL
Mapped[Optional[int]]INTEGERNULL
Mapped[str]VARCHARNOT NULL
Mapped[Optional[str]]VARCHARNULL
Mapped[bool]BOOLEANNOT NULL
Mapped[datetime]DATETIMENOT NULL
Mapped[float]FLOATNOT NULL
Mapped[bytes]BLOB/BYTEANOT NULL
Creating Tables
# Create all tables defined in Base.metadata
Base.metadata.create_all(engine)

# Drop all tables
Base.metadata.drop_all(engine)

Session and CRUD Operations

Session Basics
from sqlalchemy.orm import Session, sessionmaker

# Option 1: Direct Session usage
with Session(engine) as session:
    # ... operations
    session.commit()

# Option 2: Using sessionmaker (recommended for applications)
SessionFactory = sessionmaker(bind=engine)

with SessionFactory() as session:
    # ... operations
    session.commit()

# Option 3: With explicit begin/commit/rollback
with Session(engine) as session:
    with session.begin():
        # Automatically commits on success, rolls back on exception
        session.add(some_object)
Create (INSERT)
with Session(engine) as session:
    # Create single object
    user = User(name="alice", fullname="Alice Smith")
    session.add(user)

    # Create with related objects
    user_with_addresses = User(
        name="bob",
        fullname="Bob Jones",
        addresses=[
            Address(email_address="bob@example.com"),
            Address(email_address="bob@work.com"),
        ]
    )
    session.add(user_with_addresses)

    # Add multiple objects
    session.add_all([
        User(name="carol"),
        User(name="dave"),
    ])

    session.commit()
Read (SELECT)
from sqlalchemy import select

with Session(engine) as session:
    # Get by primary key
    user = session.get(User, 1)

    # Select all
    stmt = select(User)
    users = session.scalars(stmt).all()

    # Select with filter
    stmt = select(User).where(User.name == "alice")
    alice = session.scalars(stmt).first()

    # Select with multiple conditions
    stmt = select(User).where(
        User.name.like("a%"),
        User.id > 5
    )

    # Select specific columns
    stmt = select(User.name, User.fullname)
    rows = session.execute(stmt).all()
    for name, fullname in rows:
        print(f"{name}: {fullname}")

    # Order by
    stmt = select(User).order_by(User.name.desc())

    # Limit and offset
    stmt = select(User).limit(10).offset(20)

    # Count
    from sqlalchemy import func
    stmt = select(func.count()).select_from(User)
    count = session.scalar(stmt)
Update
with Session(engine) as session:
    # Update via ORM (load then modify)
    user = session.get(User, 1)
    user.fullname = "Alice Johnson"
    session.commit()

    # Bulk update
    from sqlalchemy import update
    stmt = update(User).where(User.name == "alice").values(fullname="Alice Updated")
    session.execute(stmt)
    session.commit()
Delete
with Session(engine) as session:
    # Delete via ORM
    user = session.get(User, 1)
    session.delete(user)
    session.commit()

    # Bulk delete
    from sqlalchemy import delete
    stmt = delete(User).where(User.name == "alice")
    session.execute(stmt)
    session.commit()

Relationships

One-to-Many / Many-to-One
class Parent(Base):
    __tablename__ = "parent"

    id: Mapped[int] = mapped_column(primary_key=True)
    children: Mapped[List["Child"]] = relationship(back_populates="parent")


class Child(Base):
    __tablename__ = "child"

    id: Mapped[int] = mapped_column(primary_key=True)
    parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))
    parent: Mapped["Parent"] = relationship(back_populates="children")
One-to-One
class User(Base):
    __tablename__ = "user"

    id: Mapped[int] = mapped_column(primary_key=True)
    profile: Mapped["Profile"] = relationship(back_populates="user", uselist=False)


class Profile(Base):
    __tablename__ = "profile"

    id: Mapped[int] = mapped_column(primary_key=True)
    user_id: Mapped[int] = mapped_column(ForeignKey("user.id"), unique=True)
    user: Mapped["User"] = relationship(back_populates="profile")
Many-to-Many
from sqlalchemy import Column, Table

# Association table (no ORM class needed)
association_table = Table(
    "association",
    Base.metadata,
    Column("left_id", ForeignKey("left.id"), primary_key=True),
    Column("right_id", ForeignKey("right.id"), primary_key=True),
)


class Left(Base):
    __tablename__ = "left"

    id: Mapped[int] = mapped_column(primary_key=True)
    rights: Mapped[List["Right"]] = relationship(
        secondary=association_table,
        back_populates="lefts"
    )


class Right(Base):
    __tablename__ = "right"

    id: Mapped[int] = mapped_column(primary_key=True)
    lefts: Mapped[List["Left"]] = relationship(
        secondary=association_table,
        back_populates="rights"
    )
Association Object (Many-to-Many with extra data)
class Association(Base):
    __tablename__ = "association"

    left_id: Mapped[int] = mapped_column(ForeignKey("left.id"), primary_key=True)
    right_id: Mapped[int] = mapped_column(ForeignKey("right.id"), primary_key=True)
    extra_data: Mapped[Optional[str]]

    left: Mapped["Left"] = relationship(back_populates="right_associations")
    right: Mapped["Right"] = relationship(back_populates="left_associations")


class Left(Base):
    __tablename__ = "left"

    id: Mapped[int] = mapped_column(primary_key=True)
    right_associations: Mapped[List["Association"]] = relationship(back_populates="left")


class Right(Base):
    __tablename__ = "right"

    id: Mapped[int] = mapped_column(primary_key=True)
    left_associations: Mapped[List["Association"]] = relationship(back_populates="right")

Loading Strategies

Lazy Loading (Default)
# Lazy loading - queries database when attribute is accessed
user = session.get(User, 1)
# SELECT ... FROM user WHERE id = 1

addresses = user.addresses  # N+1 query problem!
# SELECT ... FROM address WHERE user_id = 1
Eager Loading with joinedload
from sqlalchemy.orm import joinedload

# Load user and addresses in single query using JOIN
stmt = select(User).options(joinedload(User.addresses)).where(User.id == 1)
user = session.scalars(stmt).unique().first()
# SELECT ... FROM user LEFT OUTER JOIN address ON ...

from sqlalchemy.orm import selectinload

# Load users, then load all addresses with IN clause
stmt = select(User).options(selectinload(User.addresses))
users = session.scalars(stmt).all()
# SELECT ... FROM user
# SELECT ... FROM address WHERE user_id IN (1, 2, 3, ...)
Raise on Lazy Load (Prevent N+1)
from sqlalchemy.orm import raiseload

# Raise error if lazy loading is attempted
stmt = select(User).options(raiseload(User.addresses))
user = session.scalars(stmt).first()
user.addresses  # Raises InvalidRequestError
Setting Default Loading Strategy
class User(Base):
    __tablename__ = "user"

    id: Mapped[int] = mapped_column(primary_key=True)
    # Always eager load addresses
    addresses: Mapped[List["Address"]] = relationship(lazy="selection")

Joins and Complex Queries

from sqlalchemy import select, and_, or_, func

# Explicit JOIN
stmt = (
    select(User, Address)
    .join(Address, User.id == Address.user_id)
    .where(User.name == "alice")
)

# JOIN using relationship
stmt = (
    select(
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Dateimetadaten
name: sqlalchemy
description: Python SQL toolkit and Object Relational Mapper (ORM). Use when working with databases in Python, defining models, building queries, managing sessions, or interacting with SQL databases using Python objects.
license: MIT
Originaltext anzeigen
---
name: sqlalchemy
description: Python SQL toolkit and Object Relational Mapper (ORM). Use when working with databases in Python, defining models, building queries, managing sessions, or interacting with SQL databases using Python objects.
license: MIT
---

# SQLAlchemy Skill

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that provides the full power and flexibility of SQL. It consists of two main components: **Core** (SQL Expression Language) and **ORM** (Object Relational Mapper).

## When to Use This Skill

Use SQLAlchemy when:
- Working with relational databases in Python
- Defining database models as Python classes
- Building SQL queries programmatically
- Managing database transactions and sessions
- Mapping Python objects to database tables
- Need database-agnostic code that works across PostgreSQL, MySQL, SQLite, etc.

## Installation

```bash
pip install sqlalchemy

# For async support
pip install sqlalchemy[asyncio]
```

## Architecture Overview

```
┌─────────────────────────────────────────────────────────────┐
│                    SQLAlchemy ORM                           │
│  (Declarative Mapping, Session, Relationships, Unit of Work)│
├─────────────────────────────────────────────────────────────┤
│                   SQLAlchemy Core                           │
│     (SQL Expression Language, Engine, Connection Pool)      │
├─────────────────────────────────────────────────────────────┤
│                        DBAPI                                │
│         (psycopg2, pymysql, sqlite3, etc.)                  │
└─────────────────────────────────────────────────────────────┘
```

## Engine and Connection

The Engine is the starting point for SQLAlchemy applications:

```python
from sqlalchemy import create_engine

# SQLite (in-memory)
engine = create_engine("sqlite://", echo=True)

# SQLite (file-based)
engine = create_engine("sqlite:///mydatabase.db")

# PostgreSQL
engine = create_engine("postgresql+psycopg2://user:password@localhost/dbname")

# MySQL
engine = create_engine("mysql+pymysql://user:password@localhost/dbname")

# Connection pool settings
engine = create_engine(
    "postgresql+psycopg2://user:password@localhost/dbname",
    pool_size=5,           # Number of connections to keep open
    max_overflow=10,       # Additional connections allowed
    pool_timeout=30,       # Seconds to wait for connection
    pool_recycle=1800,     # Recycle connections after N seconds
)
```

### Using Connections Directly (Core)

```python
from sqlalchemy import text

with engine.connect() as conn:
    result = conn.execute(text("SELECT * FROM users WHERE id = :id"), {"id": 1})
    for row in result:
        print(row)

    # For write operations, commit explicitly
    conn.execute(text("INSERT INTO users (name) VALUES (:name)"), {"name": "Alice"})
    conn.commit()
```

## ORM Declarative Mapping


```python
from datetime import datetime
from typing import List, Optional

from sqlalchemy import ForeignKey, String, func
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship


class Base(DeclarativeBase):
    pass


class User(Base):
    __tablename__ = "user_account"

    # Primary key with auto-increment
    id: Mapped[int] = mapped_column(primary_key=True)

    # Required string column with max length
    name: Mapped[str] = mapped_column(String(30))

    # Optional column (nullable)
    fullname: Mapped[Optional[str]]

    # Column with default value
    created_at: Mapped[datetime] = mapped_column(default=func.now())

    # One-to-many relationship
    addresses: Mapped[List["Address"]] = relationship(
        back_populates="user",
        cascade="all, delete-orphan"
    )

    def __repr__(self) -> str:
        return f"User(id={self.id!r}, name={self.name!r})"


class Address(Base):
    __tablename__ = "address"

    id: Mapped[int] = mapped_column(primary_key=True)
    email_address: Mapped[str]
    user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))

    # Many-to-one relationship (back reference)
    user: Mapped["User"] = relationship(back_populates="addresses")

    def __repr__(self) -> str:
        return f"Address(id={self.id!r}, email_address={self.email_address!r})"
```

### Type Annotation Guide

| Python Type | SQL Type | Nullable |
|-------------|----------|----------|
| `Mapped[int]` | INTEGER | NOT NULL |
| `Mapped[Optional[int]]` | INTEGER | NULL |
| `Mapped[str]` | VARCHAR | NOT NULL |
| `Mapped[Optional[str]]` | VARCHAR | NULL |
| `Mapped[bool]` | BOOLEAN | NOT NULL |
| `Mapped[datetime]` | DATETIME | NOT NULL |
| `Mapped[float]` | FLOAT | NOT NULL |
| `Mapped[bytes]` | BLOB/BYTEA | NOT NULL |

### Creating Tables

```python
# Create all tables defined in Base.metadata
Base.metadata.create_all(engine)

# Drop all tables
Base.metadata.drop_all(engine)
```

## Session and CRUD Operations

### Session Basics

```python
from sqlalchemy.orm import Session, sessionmaker

# Option 1: Direct Session usage
with Session(engine) as session:
    # ... operations
    session.commit()

# Option 2: Using sessionmaker (recommended for applications)
SessionFactory = sessionmaker(bind=engine)

with SessionFactory() as session:
    # ... operations
    session.commit()

# Option 3: With explicit begin/commit/rollback
with Session(engine) as session:
    with session.begin():
        # Automatically commits on success, rolls back on exception
        session.add(some_object)
```

### Create (INSERT)

```python
with Session(engine) as session:
    # Create single object
    user = User(name="alice", fullname="Alice Smith")
    session.add(user)

    # Create with related objects
    user_with_addresses = User(
        name="bob",
        fullname="Bob Jones",
        addresses=[
            Address(email_address="bob@example.com"),
            Address(email_address="bob@work.com"),
        ]
    )
    session.add(user_with_addresses)

    # Add multiple objects
    session.add_all([
        User(name="carol"),
        User(name="dave"),
    ])

    session.commit()
```

### Read (SELECT)

```python
from sqlalchemy import select

with Session(engine) as session:
    # Get by primary key
    user = session.get(User, 1)

    # Select all
    stmt = select(User)
    users = session.scalars(stmt).all()

    # Select with filter
    stmt = select(User).where(User.name == "alice")
    alice = session.scalars(stmt).first()

    # Select with multiple conditions
    stmt = select(User).where(
        User.name.like("a%"),
        User.id > 5
    )

    # Select specific columns
    stmt = select(User.name, User.fullname)
    rows = session.execute(stmt).all()
    for name, fullname in rows:
        print(f"{name}: {fullname}")

    # Order by
    stmt = select(User).order_by(User.name.desc())

    # Limit and offset
    stmt = select(User).limit(10).offset(20)

    # Count
    from sqlalchemy import func
    stmt = select(func.count()).select_from(User)
    count = session.scalar(stmt)
```

### Update

```python
with Session(engine) as session:
    # Update via ORM (load then modify)
    user = session.get(User, 1)
    user.fullname = "Alice Johnson"
    session.commit()

    # Bulk update
    from sqlalchemy import update
    stmt = update(User).where(User.name == "alice").values(fullname="Alice Updated")
    session.execute(stmt)
    session.commit()
```

### Delete

```python
with Session(engine) as session:
    # Delete via ORM
    user = session.get(User, 1)
    session.delete(user)
    session.commit()

    # Bulk delete
    from sqlalchemy import delete
    stmt = delete(User).where(User.name == "alice")
    session.execute(stmt)
    session.commit()
```

## Relationships

### One-to-Many / Many-to-One

```python
class Parent(Base):
    __tablename__ = "parent"

    id: Mapped[int] = mapped_column(primary_key=True)
    children: Mapped[List["Child"]] = relationship(back_populates="parent")


class Child(Base):
    __tablename__ = "child"

    id: Mapped[int] = mapped_column(primary_key=True)
    parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))
    parent: Mapped["Parent"] = relationship(back_populates="children")
```

### One-to-One

```python
class User(Base):
    __tablename__ = "user"

    id: Mapped[int] = mapped_column(primary_key=True)
    profile: Mapped["Profile"] = relationship(back_populates="user", uselist=False)


class Profile(Base):
    __tablename__ = "profile"

    id: Mapped[int] = mapped_column(primary_key=True)
    user_id: Mapped[int] = mapped_column(ForeignKey("user.id"), unique=True)
    user: Mapped["User"] = relationship(back_populates="profile")
```

### Many-to-Many

```python
from sqlalchemy import Column, Table

# Association table (no ORM class needed)
association_table = Table(
    "association",
    Base.metadata,
    Column("left_id", ForeignKey("left.id"), primary_key=True),
    Column("right_id", ForeignKey("right.id"), primary_key=True),
)


class Left(Base):
    __tablename__ = "left"

    id: Mapped[int] = mapped_column(primary_key=True)
    rights: Mapped[List["Right"]] = relationship(
        secondary=association_table,
        back_populates="lefts"
    )


class Right(Base):
    __tablename__ = "right"

    id: Mapped[int] = mapped_column(primary_key=True)
    lefts: Mapped[List["Left"]] = relationship(
        secondary=association_table,
        back_populates="rights"
    )
```

### Association Object (Many-to-Many with extra data)

```python
class Association(Base):
    __tablename__ = "association"

    left_id: Mapped[int] = mapped_column(ForeignKey("left.id"), primary_key=True)
    right_id: Mapped[int] = mapped_column(ForeignKey("right.id"), primary_key=True)
    extra_data: Mapped[Optional[str]]

    left: Mapped["Left"] = relationship(back_populates="right_associations")
    right: Mapped["Right"] = relationship(back_populates="left_associations")


class Left(Base):
    __tablename__ = "left"

    id: Mapped[int] = mapped_column(primary_key=True)
    right_associations: Mapped[List["Association"]] = relationship(back_populates="left")


class Right(Base):
    __tablename__ = "right"

    id: Mapped[int] = mapped_column(primary_key=True)
    left_associations: Mapped[List["Association"]] = relationship(back_populates="right")
```

## Loading Strategies

### Lazy Loading (Default)

```python
# Lazy loading - queries database when attribute is accessed
user = session.get(User, 1)
# SELECT ... FROM user WHERE id = 1

addresses = user.addresses  # N+1 query problem!
# SELECT ... FROM address WHERE user_id = 1
```

### Eager Loading with joinedload

```python
from sqlalchemy.orm import joinedload

# Load user and addresses in single query using JOIN
stmt = select(User).options(joinedload(User.addresses)).where(User.id == 1)
user = session.scalars(stmt).unique().first()
# SELECT ... FROM user LEFT OUTER JOIN address ON ...
```

### Eager Loading with selectinload (Recommended)

```python
from sqlalchemy.orm import selectinload

# Load users, then load all addresses with IN clause
stmt = select(User).options(selectinload(User.addresses))
users = session.scalars(stmt).all()
# SELECT ... FROM user
# SELECT ... FROM address WHERE user_id IN (1, 2, 3, ...)
```

### Raise on Lazy Load (Prevent N+1)

```python
from sqlalchemy.orm import raiseload

# Raise error if lazy loading is attempted
stmt = select(User).options(raiseload(User.addresses))
user = session.scalars(stmt).first()
user.addresses  # Raises InvalidRequestError
```

### Setting Default Loading Strategy

```python
class User(Base):
    __tablename__ = "user"

    id: Mapped[int] = mapped_column(primary_key=True)
    # Always eager load addresses
    addresses: Mapped[List["Address"]] = relationship(lazy="selection")
```

## Joins and Complex Queries

```python
from sqlalchemy import select, and_, or_, func

# Explicit JOIN
stmt = (
    select(User, Address)
    .join(Address, User.id == Address.user_id)
    .where(User.name == "alice")
)

# JOIN using relationship
stmt = (
    select(

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  • Permission surface: secrets or environment access, shell or command execution
Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

  1. 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
  2. 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
  3. 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
pavelzw/skill-forge
Lizenz
MIT
Version
Unknown
Letzter GitHub-Push
12. Sept. 2026
Verzeichnis aktualisiert
13. Sept. 2026
Anleitungspfad
recipes/sqlalchemy/SKILL.md @ 65c60c1fc8dc

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

55/100

Vielversprechend

Vertrauen

57/100

Do not auto-install

Audit

70/100

Prüfung nötig

Vollständiges Audit öffnenEval-Bericht ansehen
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • Low GitHub adoption signal
  • KI-Prüffreigabe fehlt
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 24 GitHub stars
  • Stars/forks activity: 24 stars, 10 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Verified installs
—
Ergebnisse
—

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnenLLM-TextformatManifest öffnenAuto-Auflösungsplan
Weitere Details
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        "id": "codex",
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Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller
pavelzw
Indexiert von
OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

Diesen Skill beanspruchen

Eigentümeranspruch

Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird pavelzw zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

Share-Kit

Creator-Backlink-Kit

Evidenz-Badges in deine README einfügen

Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/pavelzw-sqlalchemy?metric=listed&label=Listed)](https://www.openagentskill.com/skills/pavelzw-sqlalchemy?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/pavelzw-sqlalchemy?metric=trust&label=Trust)](https://www.openagentskill.com/skills/pavelzw-sqlalchemy?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/pavelzw-sqlalchemy?metric=audit&label=Audit)](https://www.openagentskill.com/skills/pavelzw-sqlalchemy/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/pavelzw-sqlalchemy?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/pavelzw-sqlalchemy?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
Badge-Vorschau Audit öffnen Creator-Kit

Community-Signal

Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.