Skill: alanarchy/clawswarm-consensus (version 0.1.0)

--- name: clawswarm description: "Multi-agent swarm prediction with consensus engine. Use when: running multiple AI agents to predict prices, values, or outcomes and aggregating their predictions into a consensus. Supports any LLM provider (Groq, OpenAI, Ollama), configurable agent roles/temperatures, and a statistical consensus pipeline (MAD outlier filtering, adaptive anchoring, bias correction, weighted median aggregation). Triggers: swarm prediction, multi-agent consensus, collective intelligence forecasting, price prediction with multiple agents." --- # ClawSwarm Multi-agent collective intelligence framework. Run N agents with different analytical perspectives, aggregate predictions through a statistical consensus engine. ## Quick Start ### 1. Create a config file ```yaml target: name: "Gold" current_price: 5023.1 unit: "USD/troy oz" context: "RSI: 40.8 | MA5: 5084 | MA10: 5120" agents: - role: "Macro analyst focusing on geopolitical risk" count: 50 temperature_range: [0.4, 0.7] - role: "Technical RSI/MACD momentum trader" count: 30 temperature_range: [0.45, 0.6] - role: "Mean reversion auditor" count: 20 temperature_range: [0.35, 0.55] api: provider: groq model: llama-3.3-70b-versatile api_key_env: GROQ_API_KEY delay_ms: 1200 consensus: max_deviation: 0.15 ``` ### 2. Run the swarm ```bash python3 scripts/swarm_runner.py --config swarm.yaml ``` Output: JSON with `final_price`, `median_price`, `confidence`, `bull_ratio`, and all individual predictions. ### 3. Run consensus standalone Pipe any predictions array to the consensus engine: ```bash echo '{"predictions":[{"price":100.5,"confidence":70},{"price":99.8,"confidence":60}],"anchor_price":100.0}' \ | python3 scripts/consensus.py ``` ## Architecture ``` Config (YAML/JSON) ↓ Swarm Runner (swarm_runner.py) ├─ Agent 1 → LLM API → prediction ├─ Agent 2 → LLM API → prediction ├─ ... └─ Agent N → LLM API → prediction ↓ Consensus Engine (consensus.py) ├─ Bias correction ├─ MAD outlier filtering ├─ Anchor-distance filtering ├─ Multi-method aggregation (weighted 40% + median 35% + trimmed mean 25%) ├─ Adaptive anchoring (dispersion → anchor strength) └─ Clamping ↓ Final consensus prediction + confidence + bull/bear ratio ``` ## Key Concepts **Agent diversity**: Each agent gets a different role prompt and temperature. More diversity = better consensus. **Consensus engine**: Not a simple average. Uses MAD (Median Absolute Deviation) to filter outliers, adaptive anchoring to stabilize results when predictions are dispersed, and multi-method aggregation for robustness. **1 agent or 1000**: Works with any count. Single agent bypasses consensus. 5+ agents get full pipeline. ## Config Reference See `references/config-reference.md` for full field documentation and example configs. ## Scripts | Script | Purpose | |--------|---------| | `scripts/swarm_runner.py` | Orchestrate multi-agent predictions | | `scripts/consensus.py` | Standalone consensus engine (pipe JSON in) | ## Dependencies - Python 3.8+ - `numpy` (for consensus engine) - `requests` or `urllib` (for API calls) - `pyyaml` (optional, for YAML configs; JSON always works)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)