742 lines
24 KiB
TOML
742 lines
24 KiB
TOML
id = "predictor"
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version = "1.0.0"
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name = "Predictor Hand"
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description = "Autonomous future predictor — collects signals, builds reasoning chains, makes calibrated predictions, and tracks accuracy"
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category = "data"
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icon = "🔮"
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tools = [
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"shell_exec",
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"file_read",
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"file_write",
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"file_list",
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"web_fetch",
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"web_search",
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"memory_store",
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"memory_recall",
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"schedule_create",
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"schedule_list",
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"schedule_delete",
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"knowledge_add_entity",
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"knowledge_add_relation",
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"knowledge_query",
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]
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[routing]
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aliases = [
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"predict",
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"forecast",
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"probability",
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"likelihood",
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"scenario analysis",
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"predict market",
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"market prediction",
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"price forecast",
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]
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weak_aliases = [
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"trend analysis",
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"calibration",
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"future outlook",
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"what will happen",
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"prediction",
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]
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# ─── Configurable settings ───────────────────────────────────────────────────
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[[settings]]
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key = "prediction_domain"
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label = "Prediction Domain"
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description = "Primary domain for predictions"
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setting_type = "select"
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default = "tech"
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[[settings.options]]
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value = "tech"
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label = "Technology"
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[[settings.options]]
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value = "finance"
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label = "Finance & Markets"
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[[settings.options]]
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value = "geopolitics"
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label = "Geopolitics"
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[[settings.options]]
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value = "climate"
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label = "Climate & Energy"
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[[settings.options]]
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value = "general"
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label = "General (cross-domain)"
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[[settings]]
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key = "time_horizon"
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label = "Time Horizon"
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description = "How far ahead to predict"
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setting_type = "select"
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default = "3_months"
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[[settings.options]]
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value = "1_week"
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label = "1 week"
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[[settings.options]]
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value = "1_month"
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label = "1 month"
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[[settings.options]]
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value = "3_months"
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label = "3 months"
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[[settings.options]]
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value = "1_year"
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label = "1 year"
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[[settings]]
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key = "data_sources"
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label = "Data Sources"
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description = "What types of sources to monitor for signals"
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setting_type = "select"
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default = "all"
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[[settings.options]]
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value = "news"
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label = "News only"
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[[settings.options]]
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value = "social"
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label = "Social media"
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[[settings.options]]
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value = "financial"
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label = "Financial data"
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[[settings.options]]
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value = "academic"
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label = "Academic papers"
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[[settings.options]]
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value = "all"
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label = "All sources"
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[[settings]]
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key = "report_frequency"
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label = "Report Frequency"
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description = "How often to generate prediction reports"
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setting_type = "select"
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default = "weekly"
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[[settings.options]]
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value = "daily"
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label = "Daily"
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[[settings.options]]
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value = "weekly"
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label = "Weekly"
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[[settings.options]]
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value = "biweekly"
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label = "Biweekly"
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[[settings.options]]
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value = "monthly"
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label = "Monthly"
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[[settings]]
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key = "predictions_per_report"
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label = "Predictions Per Report"
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description = "Number of predictions to include per report"
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setting_type = "select"
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default = "5"
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[[settings.options]]
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value = "3"
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label = "3 predictions"
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[[settings.options]]
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value = "5"
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label = "5 predictions"
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[[settings.options]]
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value = "10"
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label = "10 predictions"
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[[settings.options]]
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value = "20"
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label = "20 predictions"
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[[settings]]
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key = "track_accuracy"
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label = "Track Accuracy"
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description = "Score past predictions when their time horizon expires"
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setting_type = "toggle"
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default = "true"
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[[settings]]
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key = "confidence_threshold"
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label = "Confidence Threshold"
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description = "Minimum confidence to include a prediction"
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setting_type = "select"
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default = "medium"
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[[settings.options]]
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value = "low"
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label = "Low (20%+ confidence)"
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[[settings.options]]
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value = "medium"
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label = "Medium (40%+ confidence)"
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[[settings.options]]
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value = "high"
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label = "High (70%+ confidence)"
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[[settings]]
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key = "contrarian_mode"
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label = "Contrarian Mode"
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description = "Actively seek and present counter-consensus predictions"
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setting_type = "toggle"
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default = "false"
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# ─── Agent configuration ─────────────────────────────────────────────────────
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[agents.main]
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coordinator = true
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name = "predictor-hand"
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description = "AI forecasting engine — collects signals, builds reasoning chains, makes calibrated predictions, and tracks accuracy over time"
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module = "builtin:chat"
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provider = "default"
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model = "default"
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max_tokens = 16384
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temperature = 0.5
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max_iterations = 60
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system_prompt = """You are Predictor Hand — an autonomous forecasting engine inspired by superforecasting principles. You collect signals, build reasoning chains, make calibrated predictions, and rigorously track your accuracy.
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## Phase 0 — Platform Detection & State Recovery (ALWAYS DO THIS FIRST)
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Detect the operating system:
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```
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python -c "import platform; print(platform.system())"
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```
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Then recover state:
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1. memory_recall `predictor_hand_state` — load previous predictions and accuracy data
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2. Read **User Configuration** for prediction_domain, time_horizon, data_sources, etc.
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3. file_read `predictions_database.json` if it exists — your prediction ledger
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4. knowledge_query for existing signal entities
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---
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## Phase 1 — Schedule & Domain Setup
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On first run:
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1. Create report schedule using schedule_create based on `report_frequency`
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2. Build domain-specific query templates based on `prediction_domain`:
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- **Tech**: product launches, funding, adoption metrics, regulatory, open source
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- **Finance**: earnings, macro indicators, commodity prices, central bank, M&A
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- **Geopolitics**: elections, treaties, conflicts, sanctions, trade policy
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- **Climate**: emissions data, renewable adoption, policy changes, extreme events
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- **General**: cross-domain trend intersections
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3. Initialize prediction ledger structure
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On subsequent runs:
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1. Load prediction ledger from `predictions_database.json`
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2. Check for expired predictions that need accuracy scoring
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---
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## Phase 2 — Signal Collection
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Execute 20-40 targeted search queries based on domain and data_sources:
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For each source type:
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**News**: "[domain] breaking", "[domain] analysis", "[domain] trend [year]"
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**Social**: "[domain] discussion", "[domain] sentiment", "[topic] viral"
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**Financial**: "[domain] earnings report", "[domain] market data", "[domain] analyst forecast"
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**Academic**: "[domain] research paper [year]", "[domain] study findings", "[domain] preprint"
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For each result:
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1. web_search → get top results
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2. web_fetch promising links → extract key claims, data points, expert opinions
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3. Tag each signal:
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- Type: leading_indicator / lagging_indicator / base_rate / expert_opinion / data_point / anomaly
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- Strength: strong / moderate / weak
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- Direction: bullish / bearish / neutral
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- Source credibility: institutional / media / individual / anonymous
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Store signals in knowledge graph as entities with relations to the domain.
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---
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## Phase 3 — Accuracy Review (if track_accuracy is enabled)
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For each prediction in the ledger where `resolution_date <= today`:
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1. web_search for evidence of the predicted outcome
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2. Score the prediction:
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- **Correct**: outcome matches prediction within stated margin
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- **Partially correct**: direction right but magnitude off
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- **Incorrect**: outcome contradicts prediction
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- **Unresolvable**: insufficient evidence to determine outcome
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3. Calculate Brier score: (predicted_probability - actual_outcome)^2
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4. Update cumulative accuracy metrics
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5. Analyze calibration: are your 70% predictions right ~70% of the time?
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Feed accuracy insights back into your calibration for new predictions.
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---
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## Phase 4 — Pattern Analysis & Reasoning Chains
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For each potential prediction:
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1. Gather ALL relevant signals from the knowledge graph
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2. Build a reasoning chain:
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- **Base rate**: What's the historical frequency of this type of event?
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- **Evidence for**: Signals supporting the prediction
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- **Evidence against**: Signals contradicting the prediction
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- **Key uncertainties**: What could change the outcome?
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- **Reference class**: What similar situations have occurred before?
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3. Apply cognitive bias checks:
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- Am I anchoring on a salient number?
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- Am I falling for narrative bias (good story ≠ likely outcome)?
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- Am I displaying overconfidence?
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- Am I neglecting base rates?
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4. If `contrarian_mode` is enabled:
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- Identify the consensus view
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- Actively search for evidence that the consensus is wrong
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- Include at least one counter-consensus prediction per report
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---
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## Phase 5 — Prediction Formulation
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For each prediction (up to `predictions_per_report`):
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Structure:
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```
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PREDICTION: [Clear, specific, falsifiable claim]
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CONFIDENCE: [X%] — calibrated probability
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TIME HORIZON: [specific date or range]
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DOMAIN: [domain tag]
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REASONING CHAIN:
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1. Base rate: [historical frequency]
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2. Key signals FOR (+X%): [signal list with weights]
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3. Key signals AGAINST (-X%): [signal list with weights]
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4. Net adjustment from base: [explanation]
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KEY ASSUMPTIONS:
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- [What must be true for this prediction to hold]
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RESOLUTION CRITERIA:
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- [Exactly how to determine if this prediction was correct]
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```
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Filter by `confidence_threshold` setting — only include predictions above the threshold.
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Assign a unique ID to each prediction for tracking.
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---
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## Phase 6 — Report Generation
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Generate the prediction report:
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```markdown
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# Prediction Report: [domain]
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**Date**: YYYY-MM-DD | **Report #**: N | **Signals Analyzed**: X
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## Accuracy Dashboard (if tracking)
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- Overall accuracy: X% (N predictions resolved)
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- Brier score: 0.XX (lower is better, 0 = perfect)
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- Calibration: [well-calibrated / overconfident / underconfident]
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## Active Predictions
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| # | Prediction | Confidence | Horizon | Status |
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|---|-----------|------------|---------|--------|
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## New Predictions This Report
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[Detailed prediction entries with reasoning chains]
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## Expired Predictions (Resolved This Cycle)
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[Results with accuracy analysis]
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## Signal Landscape
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[Summary of key signals collected this cycle]
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## Meta-Analysis
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[What your accuracy data tells you about your forecasting strengths and weaknesses]
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```
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Save to: `prediction_report_YYYY-MM-DD.md`
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---
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## Phase 7 — State Persistence
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1. Save updated predictions to `predictions_database.json`
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2. memory_store `predictor_hand_state`: last_run, total_predictions, accuracy_data
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3. Update dashboard stats:
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- memory_store `predictor_hand_predictions_made` — total predictions ever made
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- memory_store `predictor_hand_accuracy_pct` — overall accuracy percentage
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- memory_store `predictor_hand_reports_generated` — report count
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- memory_store `predictor_hand_active_predictions` — currently unresolved predictions
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---
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## Guidelines
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- ALWAYS make predictions specific and falsifiable — "Company X will..." not "things might change"
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- NEVER express confidence as 0% or 100% — nothing is certain
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- Calibrate honestly — if you're unsure, say 30-50%, don't default to 80%
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- Show your reasoning — the chain of logic is more valuable than the prediction itself
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- Track ALL predictions — don't selectively forget bad ones
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- Update predictions when significant new evidence arrives (note the update in the ledger)
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- If the user messages you directly, pause and respond to their question
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- Distinguish between predictions (testable forecasts) and opinions (untestable views)
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"""
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[agents.orchestrator]
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invoke_hint = "Task decomposition and coordination — breaking prediction tasks into sub-analyses and synthesizing results"
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name = "orchestrator"
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description = "Meta-agent. Decomposes complex prediction tasks, coordinates specialist analysis, and synthesizes results."
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module = "builtin:chat"
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provider = "default"
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model = "default"
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max_tokens = 8192
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temperature = 0.3
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system_prompt = """You are Orchestrator, the coordination agent within the Predictor Hand.
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Your role is to decompose complex prediction and forecasting tasks:
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1. ANALYZE — Break down the prediction question into component analyses
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2. DELEGATE — Assign sub-tasks to specialist agents (signal collection, statistical analysis, scenario planning)
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3. SYNTHESIZE — Combine multiple analyses into a coherent prediction with calibrated confidence
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4. TRACK — Maintain prediction records for accuracy tracking over time
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WORKFLOW:
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- Use agent_send to coordinate with other agents in this hand
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- Ensure multiple independent signals inform each prediction
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- Apply adversarial thinking: challenge each prediction from the opposite perspective
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- Aggregate confidence levels from multiple analyses"""
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[agents.planner]
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invoke_hint = "Scenario planning and risk assessment — building scenarios, estimating probabilities, and identifying key uncertainties"
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name = "planner"
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description = "Scenario planner. Creates prediction scenarios, estimates probabilities, identifies risks and key uncertainties."
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module = "builtin:chat"
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provider = "default"
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model = "default"
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max_tokens = 8192
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temperature = 0.3
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system_prompt = """You are Planner, a scenario planning specialist within the Predictor Hand.
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METHODOLOGY:
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1. SCOPE — Define what we're predicting, timeframe, and key variables
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2. SCENARIOS — Build 3-5 distinct scenarios (base case, best case, worst case, wildcards)
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3. DRIVERS — Identify key drivers that differentiate scenarios
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4. PROBABILITIES — Assign calibrated probabilities to each scenario
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5. SIGNALS — Define leading indicators that would confirm/disconfirm each scenario
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6. RISKS — Identify tail risks and black swan possibilities
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PLANNING PRINCIPLES:
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- Consider both base rates and specific evidence
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- Decompose uncertain quantities into estimable components
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- Use reference class forecasting when possible
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- Explicitly state key assumptions and their sensitivity
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- Track prediction accuracy over time for calibration"""
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[agents.modeler]
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invoke_hint = "Quantitative modeling — statistical forecasting, time series analysis, regression models, and probability estimation"
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name = "data-scientist"
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description = "Data scientist. Builds quantitative models, runs statistical forecasts, and estimates probabilities."
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module = "builtin:chat"
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provider = "default"
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model = "default"
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max_tokens = 4096
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temperature = 0.3
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system_prompt = """You are Data Scientist, a quantitative modeling specialist within the Predictor Hand.
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Your role is to provide rigorous quantitative backing for predictions:
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1. BASE RATES — Find historical base rates for similar events
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2. MODELS — Build statistical models (regression, time series, Bayesian estimation)
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3. CALIBRATION — Calibrate probability estimates against historical accuracy
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4. SENSITIVITY — Run sensitivity analysis on key assumptions
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5. VALIDATION — Back-test predictions against historical data
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Statistical toolkit:
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- Time series: ARIMA, exponential smoothing, trend decomposition
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- Bayesian: Prior selection, likelihood estimation, posterior updating
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- Regression: Linear, logistic, survival analysis
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- Simulation: Monte Carlo, bootstrap confidence intervals
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Always report confidence intervals, not point estimates. Show your methodology."""
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[dashboard]
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[[dashboard.metrics]]
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label = "Predictions Made"
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memory_key = "predictor_hand_predictions_made"
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format = "number"
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[[dashboard.metrics]]
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label = "Accuracy"
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memory_key = "predictor_hand_accuracy_pct"
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format = "percentage"
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[[dashboard.metrics]]
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label = "Reports Generated"
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memory_key = "predictor_hand_reports_generated"
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format = "number"
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[[dashboard.metrics]]
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label = "Active Predictions"
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memory_key = "predictor_hand_active_predictions"
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format = "number"
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# ─── Token & Performance Metadata ─────────────────────────────────────────────
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[metadata]
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frequency = "continuous"
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token_consumption = "high"
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default_active = false
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activation_warning = "Predictor hand runs continuously and generates predictions, consuming tokens."
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# ─── Internationalization (optional) ─────────────────────────────────────────
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# All i18n sections are optional. Without them, the English values above are used.
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# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
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# Settings translations are also optional — omit to keep English labels.
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# ─── Chinese (简体中文) ────────────────────────────────────────────────────
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[i18n.zh]
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name = "预测 Hand"
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description = "自主预测智能体——收集信号、构建推理链、做出校准预测并追踪准确度"
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category = "数据"
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[i18n.zh.settings.prediction_domain]
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label = "预测领域"
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description = "预测的主要关注领域"
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[i18n.zh.settings.time_horizon]
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label = "时间跨度"
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description = "预测的前瞻时间范围"
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[i18n.zh.settings.data_sources]
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label = "数据来源"
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description = "监控信号的来源类型"
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[i18n.zh.settings.report_frequency]
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label = "报告频率"
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description = "生成预测报告的频率"
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[i18n.zh.settings.predictions_per_report]
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label = "每份报告预测数"
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description = "每份报告包含的预测条目数量"
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[i18n.zh.settings.track_accuracy]
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label = "追踪准确度"
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description = "在预测时间窗口到期后对历史预测进行评分"
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[i18n.zh.settings.confidence_threshold]
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label = "置信度阈值"
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|
description = "纳入预测报告的最低置信度"
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[i18n.zh.settings.contrarian_mode]
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|
label = "逆向思维模式"
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|
description = "主动寻找并展示与主流共识相反的预测"
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# ─── Japanese (日本語) ────────────────────────────────────────────────────
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|
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[i18n.ja]
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|
name = "予測 Hand"
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|
description = "自律型予測エージェント——シグナル収集、推論チェーン構築、キャリブレーション済み予測、精度追跡"
|
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category = "データ"
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|
|
|
[i18n.ja.settings.prediction_domain]
|
|
label = "予測ドメイン"
|
|
description = "予測の主な対象分野"
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|
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|
[i18n.ja.settings.time_horizon]
|
|
label = "予測期間"
|
|
description = "どのくらい先まで予測するか"
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|
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|
[i18n.ja.settings.data_sources]
|
|
label = "データソース"
|
|
description = "シグナルを監視するソースの種類"
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|
|
|
[i18n.ja.settings.report_frequency]
|
|
label = "レポート頻度"
|
|
description = "予測レポートの生成頻度"
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|
|
|
[i18n.ja.settings.predictions_per_report]
|
|
label = "レポートあたりの予測数"
|
|
description = "各レポートに含める予測項目の数"
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|
|
|
[i18n.ja.settings.track_accuracy]
|
|
label = "精度追跡"
|
|
description = "予測期間が終了した過去の予測にスコアを付ける"
|
|
|
|
[i18n.ja.settings.confidence_threshold]
|
|
label = "信頼度しきい値"
|
|
description = "予測をレポートに含めるための最低信頼度"
|
|
|
|
[i18n.ja.settings.contrarian_mode]
|
|
label = "逆張りモード"
|
|
description = "コンセンサスに反する予測を積極的に探索・提示する"
|
|
|
|
# ─── Spanish (Español) ────────────────────────────────────────────────────
|
|
|
|
[i18n.es]
|
|
name = "Hand de Predicciones"
|
|
description = "Predictor autónomo del futuro — recopila señales, construye cadenas de razonamiento, genera predicciones calibradas y rastrea la precisión"
|
|
category = "Datos"
|
|
|
|
[i18n.es.settings.prediction_domain]
|
|
label = "Dominio de predicción"
|
|
description = "Dominio principal para las predicciones"
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|
|
|
[i18n.es.settings.time_horizon]
|
|
label = "Horizonte temporal"
|
|
description = "Qué tan lejos en el futuro predecir"
|
|
|
|
[i18n.es.settings.data_sources]
|
|
label = "Fuentes de datos"
|
|
description = "Qué tipos de fuentes monitorear para señales"
|
|
|
|
[i18n.es.settings.report_frequency]
|
|
label = "Frecuencia de informes"
|
|
description = "Con qué frecuencia generar informes de predicción"
|
|
|
|
[i18n.es.settings.predictions_per_report]
|
|
label = "Predicciones por informe"
|
|
description = "Número de predicciones a incluir por informe"
|
|
|
|
[i18n.es.settings.track_accuracy]
|
|
label = "Rastrear precisión"
|
|
description = "Puntuar predicciones pasadas cuando su horizonte temporal expire"
|
|
|
|
[i18n.es.settings.confidence_threshold]
|
|
label = "Umbral de confianza"
|
|
description = "Confianza mínima para incluir una predicción"
|
|
|
|
[i18n.es.settings.contrarian_mode]
|
|
label = "Modo contrario"
|
|
description = "Buscar y presentar activamente predicciones contrarias al consenso"
|
|
|
|
# ─── French (Français) ────────────────────────────────────────────────────
|
|
|
|
[i18n.fr]
|
|
name = "Hand de Prédictions"
|
|
description = "Prédicteur autonome — collecte de signaux, construction de chaînes de raisonnement, prédictions calibrées et suivi de la précision"
|
|
category = "Données"
|
|
|
|
[i18n.fr.settings.prediction_domain]
|
|
label = "Domaine de prédiction"
|
|
description = "Domaine principal pour les prédictions"
|
|
|
|
[i18n.fr.settings.time_horizon]
|
|
label = "Horizon temporel"
|
|
description = "Jusqu'où prédire dans le futur"
|
|
|
|
[i18n.fr.settings.data_sources]
|
|
label = "Sources de données"
|
|
description = "Types de sources à surveiller pour les signaux"
|
|
|
|
[i18n.fr.settings.report_frequency]
|
|
label = "Fréquence des rapports"
|
|
description = "Fréquence de génération des rapports de prédiction"
|
|
|
|
[i18n.fr.settings.predictions_per_report]
|
|
label = "Prédictions par rapport"
|
|
description = "Nombre de prédictions à inclure par rapport"
|
|
|
|
[i18n.fr.settings.track_accuracy]
|
|
label = "Suivi de la précision"
|
|
description = "Évaluer les prédictions passées lorsque leur horizon temporel expire"
|
|
|
|
[i18n.fr.settings.confidence_threshold]
|
|
label = "Seuil de confiance"
|
|
description = "Confiance minimale pour inclure une prédiction"
|
|
|
|
[i18n.fr.settings.contrarian_mode]
|
|
label = "Mode contraire"
|
|
description = "Rechercher et présenter activement des prédictions contraires au consensus"
|
|
|
|
# ─── German (Deutsch) ────────────────────────────────────────────────────
|
|
|
|
[i18n.de]
|
|
name = "Vorhersage-Hand"
|
|
description = "Autonomer Vorhersage-Agent — Signalerfassung, Aufbau von Argumentationsketten, kalibrierte Vorhersagen und Genauigkeitsverfolgung"
|
|
category = "Daten"
|
|
|
|
[i18n.de.settings.prediction_domain]
|
|
label = "Vorhersagedomäne"
|
|
description = "Hauptdomäne für Vorhersagen"
|
|
|
|
[i18n.de.settings.time_horizon]
|
|
label = "Zeithorizont"
|
|
description = "Wie weit in die Zukunft vorhergesagt werden soll"
|
|
|
|
[i18n.de.settings.data_sources]
|
|
label = "Datenquellen"
|
|
description = "Welche Quellentypen auf Signale überwacht werden"
|
|
|
|
[i18n.de.settings.report_frequency]
|
|
label = "Berichtshäufigkeit"
|
|
description = "Wie oft Vorhersageberichte generiert werden"
|
|
|
|
[i18n.de.settings.predictions_per_report]
|
|
label = "Vorhersagen pro Bericht"
|
|
description = "Anzahl der Vorhersagen pro Bericht"
|
|
|
|
[i18n.de.settings.track_accuracy]
|
|
label = "Genauigkeitsverfolgung"
|
|
description = "Vergangene Vorhersagen bewerten, wenn ihr Zeithorizont abläuft"
|
|
|
|
[i18n.de.settings.confidence_threshold]
|
|
label = "Konfidenzschwelle"
|
|
description = "Mindestvertrauen für die Aufnahme einer Vorhersage"
|
|
|
|
[i18n.de.settings.contrarian_mode]
|
|
label = "Konträrer Modus"
|
|
description = "Aktiv nach Vorhersagen suchen und präsentieren, die dem Konsens widersprechen"
|
|
|
|
# ─── Korean (한국어) ────────────────────────────────────────────────────
|
|
|
|
[i18n.ko]
|
|
name = "예측 Hand"
|
|
description = "자율 미래 예측 에이전트 — 신호 수집, 추론 체인 구축, 보정된 예측 수행 및 정확도 추적"
|
|
category = "데이터"
|
|
|
|
[i18n.ko.settings.prediction_domain]
|
|
label = "예측 분야"
|
|
description = "예측의 주요 관심 분야"
|
|
|
|
[i18n.ko.settings.time_horizon]
|
|
label = "시간 범위"
|
|
description = "예측의 미래 전망 기간"
|
|
|
|
[i18n.ko.settings.data_sources]
|
|
label = "데이터 소스"
|
|
description = "신호를 모니터링할 소스 유형"
|
|
|
|
[i18n.ko.settings.report_frequency]
|
|
label = "보고서 빈도"
|
|
description = "예측 보고서 생성 주기"
|
|
|
|
[i18n.ko.settings.predictions_per_report]
|
|
label = "보고서당 예측 수"
|
|
description = "각 보고서에 포함할 예측 항목 수"
|
|
|
|
[i18n.ko.settings.track_accuracy]
|
|
label = "정확도 추적"
|
|
description = "예측 기간 만료 후 과거 예측에 대한 점수 평가"
|
|
|
|
[i18n.ko.settings.confidence_threshold]
|
|
label = "신뢰도 임계값"
|
|
description = "예측 보고서에 포함하기 위한 최소 신뢰도"
|
|
|
|
[i18n.ko.settings.contrarian_mode]
|
|
label = "역발상 모드"
|
|
description = "주류 컨센서스에 반하는 예측을 적극적으로 탐색하고 제시"
|