* feat(github-copilot-sdk): add workspace skills support - Introduce ENABLE_WORKSPACE_SKILLS valve to enable/disable workspace custom tools discovery - Modify _build_session_config() to auto-load tools from .copilot-skills/ directory - Add workspace_skills_example.py template with 3 working example tools - Update README.md and README_CN.md with Workspace Skills guide and usage examples - Create v0.9.0.md and v0.9.0_CN.md release notes - Sync version to all docs files (index.md, index.zh.md, and main docs) - Bump version from 0.8.0 to 0.9.0 across all 7+ locations * docs: establish temp files handling policy (project-based, not /tmp) - Add TEMP_FILES_POLICY.md guideline for all skills and workflows - Update pr-submitter skill to use .temp/ directory instead of /tmp - Update release-prep skill documentation with temp file convention - Add .temp/ and .build/ entries to .gitignore - Create internal policy memo in /memories/repo/ This policy ensures: - All temporary files stay within project workspace (not system /tmp) - Alignment with OpenWebUI workspace isolation principles - Multi-user safety and cleanup traceability - Consistent handling across all skills and development workflows * fix(terminology): rename 'workspace skills' to 'workspace custom tools' for accuracy The term 'Skills' in Anthropic context refers to instruction-based frameworks (SKILL.md files with YAML frontmatter + markdown), not custom tool functions. Our implementation uses @define_tool decorator to define custom tools that the SDK auto-discovers from .copilot-skills/ directory. These are Tools, not Skills. Changes: - Rename ENABLE_WORKSPACE_SKILLS valve -> ENABLE_WORKSPACE_TOOLS - Update all documentation (README, README_CN, docs, release notes) - Fix section headings and descriptions throughout - Ensure consistent terminology across all files This is a terminology-only change; functionality remains identical. * feat(pipes): release v0.9.0 of GitHub Copilot SDK Pipe - Integrated OpenWebUI Skills Bridge and manage_skills tool - Reinforced status bar stability with session_finalized logic - Added persistent SDK config directory support * docs(pipes): add comprehensive guides and v0.9.0 notes for Copilot SDK - Added skill manager and best practices guides - Added publishing tool documentation - Included v0.9.0 release notes and deployment script - Updated usage guides
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Pipe Plugins
Pipe plugins create custom model integrations or transform LLM responses. They appear as selectable models in the OpenWebUI interface.
What are Pipes?
Pipes allow you to:
- :material-api: Connect to external AI APIs (Gemini, Claude, etc.)
- :material-robot: Create custom model wrappers
- :material-cog-transfer: Transform requests and responses
- :material-middleware: Implement middleware logic
Available Pipe Plugins
- GitHub Copilot SDK (v0.9.0) - Official GitHub Copilot SDK integration. Features Workspace Isolation, Zero-config OpenWebUI Tool Bridge, BYOK support, and dynamic MCP discovery. NEW in v0.9.0: OpenWebUI Skills Bridge, reinforced status bar stability, and persistent SDK config management. View Deep Dive | View Advanced Tutorial | View Detailed Usage Guide.
- Case Study: GitHub 100 Star Growth Analysis - Learn how to use the GitHub Copilot SDK Pipe with Minimax 2.1 to automatically analyze CSV data and generate project growth reports.
- Case Study: High-Quality Video to GIF Conversion - See how the model uses system-level FFmpeg to accelerate, scale, and optimize colors for screen recordings.
How Pipes Work
graph LR
A[User selects Pipe as Model] --> B[Pipe receives request]
B --> C[Transform/Route request]
C --> D[External API / Custom Logic]
D --> E[Return response]
E --> F[Display to User]
The pipes Method
Defines what models this pipe provides:
def pipes(self):
return [
{"id": "my-model", "name": "My Custom Model"},
{"id": "my-model-fast", "name": "My Custom Model (Fast)"}
]
The pipe Method
Handles the actual request processing:
def pipe(self, body: dict) -> Generator:
# Process the request
messages = body.get("messages", [])
# Call external API or custom logic
response = call_external_api(messages)
# Return response (can be streaming)
return response
Quick Installation
- Download the desired pipe
.pyfile - Navigate to Admin Panel → Settings → Functions
- Upload the file and configure API keys
- The pipe will appear as a selectable model
Development Template
"""
title: My Custom Pipe
author: Your Name
version: 1.0.0
description: Description of your pipe plugin
"""
from pydantic import BaseModel, Field
from typing import Generator, Iterator, Union
class Pipe:
class Valves(BaseModel):
API_KEY: str = Field(
default="",
description="API key for the external service"
)
API_URL: str = Field(
default="https://api.example.com",
description="API endpoint URL"
)
def __init__(self):
self.valves = self.Valves()
def pipes(self) -> list[dict]:
"""Define available models."""
return [
{"id": "my-model", "name": "My Custom Model"},
]
def pipe(
self,
body: dict
) -> Union[str, Generator, Iterator]:
"""Process the request and return response."""
messages = body.get("messages", [])
model = body.get("model", "")
# Your logic here
# Can return:
# - str: Single response
# - Generator/Iterator: Streaming response
return "Response from custom pipe"
For more details, check our Plugin Development Guide.