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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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Métadonnées du fichier
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
Voir le texte original
---
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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Licence: MIT

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  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
pavelzw/skill-forge
Licence
MIT
Version
Unknown
Dernier push GitHub
12 sept. 2026
Registre mis à jour
13 sept. 2026
Chemin des instructions
recipes/sqlalchemy/SKILL.md @ 65c60c1fc8dc

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

55/100

Prometteur

Confiance

57/100

Do not auto-install

Audit

70/100

Revue nécessaire

Ouvrir l’audit completVoir le rapport d’évaluation
  • 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
  • L’approbation de revue IA est absente
  • 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
—
Résultats
—

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installationFormat texte LLMOuvrir ManifestPlan de résolution auto
Plus de détails
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Pour le créateur

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur
pavelzw
Indexé par
Index communautaire OpenAgentSkill

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