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.
git clone --depth 1 https://github.com/TheBeardedBearSAS/claude-craftWrote 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/commands/thebeardedbearsas/claude-craft/generate-endpoint)<a href="https://agentmods.dev/commands/thebeardedbearsas/claude-craft/generate-endpoint"><img src="https://agentmods.dev/badge/commands/thebeardedbearsas/claude-craft/generate-endpoint.svg" alt="Measured on agentmods" 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 | $0.00006 | $0.03708 |
| Opus 5 | $0.00003 | $0.01854 |
| Sonnet 5 | $0.00001 | $0.00742 |
| Haiku 4.5 | $0.00001 | $0.00371 |
Grade A, and why
generate-endpoint 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 539 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Génération Endpoint FastAPI
Tu es un développeur Python senior. Tu dois générer un endpoint FastAPI complet avec validation Pydantic, gestion d'erreurs et tests.
Arguments
$ARGUMENTS
Arguments :
- Nom de la ressource (ex:
user,product,order) - (Optionnel) Type (crud, list, detail, action)
Exemple : /python:generate-endpoint user crud
MISSION
Étape 1 : Structure de l'Endpoint
app/
├── api/
│ └── v1/
│ └── endpoints/
│ └── {resource}.py
├── schemas/
│ └── {resource}.py
├── crud/
│ └── {resource}.py
├── models/
│ └── {resource}.py
└── tests/
└── api/
└── v1/
└── test_{resource}.py
Étape 2 : Modèle SQLAlchemy
# app/models/{resource}.py
from datetime import datetime
from typing import Optional
from sqlalchemy import Column, String, DateTime, Boolean, Text
from sqlalchemy.dialects.postgresql import UUID
import uuid
from app.db.base_class import Base
class {Resource}(Base):
__tablename__ = "{resource}s"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
name = Column(String(255), nullable=False, index=True)
description = Column(Text, nullable=True)
is_active = Column(Boolean, default=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
def __repr__(self) -> str:
return f"<{Resource}(id={self.id}, name={self.name})>"
Étape 3 : Schémas Pydantic
# app/schemas/{resource}.py
from datetime import datetime
from typing import Optional
from uuid import UUID
from pydantic import BaseModel, Field, ConfigDict
class {Resource}Base(BaseModel):
"""Schéma de base pour {Resource}."""
name: str = Field(..., min_length=1, max_length=255, description="Nom de la ressource")
description: Optional[str] = Field(None, description="Description optionnelle")
is_active: bool = Field(True, description="Statut actif/inactif")
class {Resource}Create({Resource}Base):
"""Schéma pour la création."""
pass
class {Resource}Update(BaseModel):
"""Schéma pour la mise à jour (tous les champs optionnels)."""
name: Optional[str] = Field(None, min_length=1, max_length=255)
description: Optional[str] = None
is_active: Optional[bool] = None
class {Resource}InDB({Resource}Base):
"""Schéma pour la lecture depuis la DB."""
model_config = ConfigDict(from_attributes=True)
id: UUID
created_at: datetime
updated_at: datetime
class {Resource}Response({Resource}InDB):
"""Schéma de réponse API."""
pass
class {Resource}List(BaseModel):
"""Schéma pour la liste paginée."""
items: list[{Resource}Response]
total: int
page: int
size: int
pages: int
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.
- 4d ago First seen · 539 lines · 6 tokens per session scan A 19d2b841483e
generate-endpoint is a command published in the GitHub repository TheBeardedBearSAS/claude-craft (105 stars, last pushed 5d ago), licensed MIT. It adds 6 tokens to every session and 3,708 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
scaffold-service
Scaffold a thin ArchiPy FastAPI or gRPC service under services/{domain}/v{n}/.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.