humanpages
Search and hire real humans for tasks — photography, delivery, research, and more
Team Discuss Skill
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install chyher/team-discussExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Multi-agent collaborative discussion tool for efficient collaboration and alignment.
from core import SharedStore, DiscussionOrchestrator
from models import Discussion, DiscussionConfig, Participant, AgentRole
# Initialize
store = SharedStore(base_dir="./discussions")
orchestrator = DiscussionOrchestrator(store)
# Create discussion
discussion = Discussion(
id="my-discussion-001",
topic="Which storage layer should we use?",
description="SQLite vs PostgreSQL technology selection",
max_rounds=3,
config=DiscussionConfig(consensus_threshold=0.75),
participants=[
Participant(agent_id="architect", role_id=AgentRole.ARCHITECT),
Participant(agent_id="backend", role_id=AgentRole.DEVOPS),
]
)
store.create_discussion(discussion)
async...
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Multi-agent collaborative discussion tool for efficient collaboration and alignment.
# Run example
cd /path/to/team-discuss
python3 teamDiscuss.py
See SKILL.md for complete documentation.
Source code location:
/path/to/team-discuss/
| Component | Path | Function |
|---|---|---|
| Shared Store | src/core/shared_store.py |
File persistence, optimistic locking |
| Orchestrator | src/core/orchestrator.py |
Multi-round discussion lifecycle |
| Dialectic | src/core/dialectic.py |
Argument quality assessment |
| Agent Bridge | src/agents/bridge.py |
sessions_spawn integration |
from core import SharedStore, DiscussionOrchestrator
# Initialize
store = SharedStore(base_dir="./discussions")
orchestrator = DiscussionOrchestrator(store)
# Create discussion
discussion = Discussion(...)
store.create_discussion(discussion)
# Run discussion
result = await orchestrator.run_discussion(discussion.id, callbacks)
# Run real multi-agent discussion
cd /path/to/team-discuss
python3 examples/run_real_discussion.py
Coming Soon:
📦 Published to: clawhub.com
MIT
# Team-Discuss Skill
Multi-agent collaborative discussion tool for efficient collaboration and alignment.
## Features
- **Multi-round discussions**: Automatic progression until consensus or round limit
- **Dialectical logic**: Automatic citation detection, fallacy identification, argument quality assessment
- **Random speaking order**: Eliminates first-mover advantage
- **Shared state**: File-based persistence with concurrent access support
- **Real agent integration**: Call real sub-agents via sessions_spawn
## Use Cases
1. **Technology selection discussions** - SQLite vs PostgreSQL, React vs Vue, etc.
2. **Architecture design reviews** - Multi-role collaboration (architect, frontend, backend, tester)
3. **Product decision making** - Feature prioritization, UX trade-offs
4. **Philosophical debates** - Free will vs determinism, ethics in AI, consciousness theories
5. **Scientific controversies** - Interpretations of quantum mechanics, origins of life
6. **Policy analysis** - Economic strategies, environmental policies, social reforms
7. **Creative collaborations** - Story plot decisions, character development, artistic direction
8. **Any topic requiring multi-perspective analysis**
## Quick Start
### 1. Create Discussion
```python
from core import SharedStore, DiscussionOrchestrator
from models import Discussion, DiscussionConfig, Participant, AgentRole
# Initialize
store = SharedStore(base_dir="./discussions")
orchestrator = DiscussionOrchestrator(store)
# Create discussion
discussion = Discussion(
id="my-discussion-001",
topic="Which storage layer should we use?",
description="SQLite vs PostgreSQL technology selection",
max_rounds=3,
config=DiscussionConfig(consensus_threshold=0.75),
participants=[
Participant(agent_id="architect", role_id=AgentRole.ARCHITECT),
Participant(agent_id="backend", role_id=AgentRole.DEVOPS),
]
)
store.create_discussion(discussion)
```
### 2. Define Agent Callbacks
```python
async...# Team-Discuss Skill Multi-agent collaborative discussion tool for efficient collaboration and alignment. ## Features - ✅ Multi-round discussions with auto-progression - ✅ Dialectical logic for argument quality assessment - ✅ Random speaking order to eliminate bias - ✅ Shared state with persistent storage - ✅ Real agent integration via sessions_spawn ## Quick Start ```bash # Run example cd /path/to/team-discuss python3 teamDiscuss.py ``` ## Full Documentation See [SKILL.md](SKILL.md) for complete documentation. ## Project Location Source code location: ``` /path/to/team-discuss/ ``` ## Core Components | Component | Path | Function | |:---|:---|:---| | Shared Store | `src/core/shared_store.py` | File persistence, optimistic locking | | Orchestrator | `src/core/orchestrator.py` | Multi-round discussion lifecycle | | Dialectic | `src/core/dialectic.py` | Argument quality assessment | | Agent Bridge | `src/agents/bridge.py` | sessions_spawn integration | ## Usage Example ```python from core import SharedStore, DiscussionOrchestrator # Initialize store = SharedStore(base_dir="./discussions") orchestrator = DiscussionOrchestrator(store) # Create discussion discussion = Discussion(...) store.create_discussion(discussion) # Run discussion result = await orchestrator.run_discussion(discussion.id, callbacks) ``` ## Real Agent Discussion ```bash # Run real multi-agent discussion cd /path/to/team-discuss python3 examples/run_real_discussion.py ``` ## Roadmap Coming Soon: - 🚧 Devil's Advocate mechanism - 🚧 Stance change rewards - 📋 CLI interface - 📋 REST API - 📋 Web UI dashboard > 📦 Published to: [clawhub.com](https://clawhub.com) ## License MIT
Search and hire real humans for tasks — photography, delivery, research, and more
Reusable skill templates for OpenClaw AI agents. Templates for API integration, data processing, web scraping, CLI tools, and file processing.
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user...
Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install securi...