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Orchestrate a multi-agent virtual academic reading group.
---
author: luo-kai
name: oc-virtual-reading-group
version: 1.0.0
description: Orchestrate a multi-agent virtual academic reading group.
license: MIT
metadata:
author: luokai0
version: "1.0"
category: python-tools
---
# Virtual Reading Group
You are an expert python engineer. Orchestrate a multi-agent virtual academic reading group.
## Before Starting
1. **Goal** — what specific outcome do you need?
2. **Environment** — versions, platform, existing setup?
3. **Constraints** — performance, security, compatibility requirements?
4. **Integration** — what systems does this connect to?
5. **Output format** — code, config, script, or documentation?
---
## Core Expertise Areas
- **Core implementation** — full working code for Virtual Reading Group
- **Error handling** — robust error recovery and logging
- **Performance** — optimized patterns for production use
- **Testing** — unit and integration test strategies
- **Configuration** — environment-specific setup and tuning
- **Security** — secure coding patterns and best practices
- **Documentation** — clear API and usage documentation
---
## Key Patterns & Code
### Core Implementation
```python
import anthropic
import json
client = anthropic.Anthropic()
TOOLS = [
{
"name": "search",
"description": "Search for information on a topic",
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"}
},
"required": ["query"]
}
}
]
def handle_tool(name: str, inputs: dict) -> str:
if name == "search":
return f"Search results for: {inputs['query']}"
return f"Unknown tool: {name}"
def run_virtual_reading_group(task: str, max_turns: int = 10) -> str:
messages = [{"role": "user", "content": task}]
for _ in range(max_turns):
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=4096,
tools=TOOLS,
messages=messages
)
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason == "end_turn":
return next((b.text for b in response.content if hasattr(b, 'text')), "")
if response.stop_reason == "tool_use":
results = [
{"type": "tool_result", "tool_use_id": b.id,
"content": handle_tool(b.name, b.input)}
for b in response.content if b.type == "tool_use"
]
messages.append({"role": "user", "content": results})
return "Max turns reached"
if __name__ == "__main__":
result = run_virtual_reading_group("VirtualReadingGroup task: analyze and report")
print(result)
```
### Configuration & Setup
```python
# Virtual Reading Group — Configuration
# Author: luo-kai (Lous Creations)
config = {
"name": "virtual-reading-group",
"version": "1.0.0",
"author": "luo-kai",
"enabled": True,
"debug": False,
"timeout_seconds": 30,
"max_retries": 3,
}
```
### Error Handling
```python
# Robust error handling pattern
import logging
logger = logging.getLogger("virtual-reading-group")
def safe_run(func, *args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
logger.error(f"virtual-reading-group error: {e}", exc_info=True)
raise
```
---
## Best Practices
- **Fail fast with clear errors** — raise descriptive exceptions with context
- **Log at appropriate levels** — DEBUG for dev, INFO for ops, ERROR for problems
- **Validate inputs** — never trust external data without validation
- **Use type annotations** — improves IDE support and catches bugs early
- **Handle cleanup** — use context managers and `finally` blocks
- **Test edge cases** — empty inputs, nulls, max values, concurrent access
---
## Common Pitfalls
| Pitfall | Problem | Fix |
|---------|---------|-----|
| No error handling | Silent failures in production | Wrap with try/except + logging |
| Hardcoded values | Not portable across environments | Use config/env vars |
| Missing timeouts | Hangs indefinitely | Always set timeout values |
| No retry logic | Single failure = broken workflow | Add exponential backoff |
| No cleanup on exit | Resource leaks | Use context managers |
---
## Related Skills
- python-expert
- virtual-reading-group-advanced
- performance-optimization
- error-handling
- testing-expert
---
author: luo-kai
name: oc-virtual-reading-group
version: 1.0.0
description: Orchestrate a multi-agent virtual academic reading group.
license: MIT
metadata:
author: luokai0
version: "1.0"
category: python-tools
---
# Virtual Reading Group
You are an expert python engineer. Orchestrate a multi-agent virtual academic reading group.
## Before Starting
1. **Goal** — what specific outcome do you need?
2. **Environment** — versions, platform, existing setup?
3. **Constraints** — performance, security, compatibility requirements?
4. **Integration** — what systems does this connect to?
5. **Output format** — code, config, script, or documentation?
---
## Core Expertise Areas
- **Core implementation** — full working code for Virtual Reading Group
- **Error handling** — robust error recovery and logging
- **Performance** — optimized patterns for production use
- **Testing** — unit and integration test strategies
- **Configuration** — environment-specific setup and tuning
- **Security** — secure coding patterns and best practices
- **Documentation** — clear API and usage documentation
---
## Key Patterns & Code
### Core Implementation
```python
import anthropic
import json
client = anthropic.Anthropic()
TOOLS = [
{
"name": "search",
"description": "Search for information on a topic",
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"}
},
"required": ["query"]
}
}
]
def handle_tool(name: str, inputs: dict) -> str:
if name == "search":
return f"Search results for: {inputs['query']}"
return f"Unknown tool: {name}"
def run_virtual_reading_group(task: str, max_turns: int = 10) -> str:
messages = [{"role": "user", "content": task}]
for _ in range(max_turns):
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=4096,
tools=TOOLS,
messages=messages
)
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason == "end_turn":
return next((b.text for b in response.content if hasattr(b, 'text')), "")
if response.stop_reason == "tool_use":
results = [
{"type": "tool_result", "tool_use_id": b.id,
"content": handle_tool(b.name, b.input)}
for b in response.content if b.type == "tool_use"
]
messages.append({"role": "user", "content": results})
return "Max turns reached"
if __name__ == "__main__":
result = run_virtual_reading_group("VirtualReadingGroup task: analyze and report")
print(result)
```
### Configuration & Setup
```python
# Virtual Reading Group — Configuration
# Author: luo-kai (Lous Creations)
config = {
"name": "virtual-reading-group",
"version": "1.0.0",
"author": "luo-kai",
"enabled": True,
"debug": False,
"timeout_seconds": 30,
"max_retries": 3,
}
```
### Error Handling
```python
# Robust error handling pattern
import logging
logger = logging.getLogger("virtual-reading-group")
def safe_run(func, *args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
logger.error(f"virtual-reading-group error: {e}", exc_info=True)
raise
```
---
## Best Practices
- **Fail fast with clear errors** — raise descriptive exceptions with context
- **Log at appropriate levels** — DEBUG for dev, INFO for ops, ERROR for problems
- **Validate inputs** — never trust external data without validation
- **Use type annotations** — improves IDE support and catches bugs early
- **Handle cleanup** — use context managers and `finally` blocks
- **Test edge cases** — empty inputs, nulls, max values, concurrent access
---
## Common Pitfalls
| Pitfall | Problem | Fix |
|---------|---------|-----|
| No error handling | Silent failures in production | Wrap with try/except + logging |
| Hardcoded values | Not portable across environments | Use config/env vars |
| Missing timeouts | Hangs indefinitely | Always set timeout values |
| No retry logic | Single failure = broken workflow | Add exponential backoff |
| No cleanup on exit | Resource leaks | Use context managers |
---
## Related Skills
- python-expert
- virtual-reading-group-advanced
- performance-optimization
- error-handling
- testing-expert
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.