Overview
In this guide, you’ll:- Run a production template (board meeting simulation)
- Understand every piece of output
- Explore temporal modes
- Customize parameters
- Export and analyze results
Time: 15 minutes | Cost: ~$0.08 | Prerequisites: Installation complete
Choose Your Template
Timepoint Pro includes 21 production templates. Let’s start withboard_meeting - a showcase scenario demonstrating core mechanisms.
List Available Templates
Template Tiers
Quick
Fast tests
- ~30s-2min runtime
- Less than $0.05 per run
- Minimal entities/timepoints
Standard
Moderate tests
- ~2-5 min runtime
- $0.05-0.20 per run
- Balanced complexity
Comprehensive
Thorough tests
- ~5-15 min runtime
- $0.20-1.00 per run
- Rich causal structure
Run the Board Meeting Template
Watch the output
You’ll see real-time progress:
What the metrics mean:
- Waveform schedule: Maps each (entity, timepoint) pair to resolution band (TENSOR/SCENE/DIALOG)
- Shadow report: Compares ADPRS predictions to actual resolution choices
- WSR (Waveform Sufficiency Ratio):
correct_predictions / total_predictions. Target: >0.7 - Divergent: Low divergence = good predictions. Target: under 15%
Understand the Output
Check Run Status
Explore Output Files
Navigate tooutput/simulations/:
Summary JSON Structure
summary_20260306_143022.json
Entity Data (JSONL)
Each line is a complete entity state at a specific timepoint:entities_20260306_143022.jsonl
JSONL format is ideal for streaming processing, ML pipelines, and fine-tuning datasets. Each line is independently parseable.
SQLite Database Schema
Query the database directly:Training Data Format
When applicable, training data is generated in prompt/completion pairs:training_20260306_143022.jsonl
Explore Temporal Modes
Timepoint Pro supports 5 temporal modes, each changing how causality works:FORWARD (Default)
Standard causality
- Causes precede effects
- Knowledge flows forward
- No time paradoxes
- Best for: Realistic simulations, business scenarios, training data
PORTAL
Backward reasoning
- Start from known endpoint
- Trace causal paths backward
- Best for: “How did we get here?” analysis, root cause investigation
BRANCHING
Counterfactual timelines
- Single decision point → multiple futures
- Each branch internally consistent
- Best for: “What if” analysis, strategy evaluation
CYCLICAL
Time loops and prophecy
- Future constrains past
- Prophecies must be fulfilled/subverted
- Best for: Generational sagas, mystical scenarios
DIRECTORIAL
Narrative structure
- Dramatic tension drives events
- Five-act structure (setup → climax → resolution)
- Best for: Story arcs, character-driven narratives
Run a PORTAL Mode Simulation
Let’s run the Mars mission example - tracing backward from mission failure:- Known endpoint (2031): Mission fails during orbital insertion
- Generate 3 candidate causes per backward step
- Score each candidate with 405B judge model (no mini-sims)
- Select best candidate and step back 1 year
- Repeat 5 times (
--portal-quick= 5 steps) - Result: Causal chain from 2026 → 2031
PORTAL output includes:
- Backward timeline (5-10 timepoints)
- Candidate scoring logs
- Pivot detection (critical decision points)
- Full causal graph from origin → failure
Run a BRANCHING Mode Simulation
Counterfactual timeline exploration:- Fortify strategy: Build defenses, conserve resources
- Explore strategy: Search for water, map terrain
- Repair strategy: Fix comms equipment, signal for rescue
All 19 mechanisms active:
castaway_colony_branching is the flagship showcase template demonstrating every mechanism from M1 (Heterogeneous Fidelity) to M19 (Knowledge Extraction).Customize Parameters
Override Default Model
Parallel Execution
Run multiple templates concurrently:Skip LLM Summaries (Faster, Cheaper)
Set Budget Limit
Dry Run (Cost Estimate)
Export and Analyze
Export to Markdown
exports/run_20260306_143022.md
Export to JSON
Query the Database
Analyze dialog patterns:Natural Language Mode
Generate simulations from plain English descriptions:Natural language mode automatically:
- Generates entities based on the scenario
- Creates a social graph with relationships
- Defines timepoints and causal structure
- Synthesizes dialog with character voices
Convergence Testing
Validate causal reasoning consistency by running the same template multiple times:- Runs
board_meeting3 times with identical parameters - Compares causal graphs across runs using Jaccard similarity
- Grades convergence: A (≥90%), B (≥80%), C (≥70%), D (≥50%), F (under 50%)
Next Steps
Temporal Modes
Deep dive into FORWARD, PORTAL, BRANCHING, CYCLICAL, DIRECTORIAL
Templates
All 21 templates with detailed descriptions
Mechanisms
The 19 mechanisms (M1-M19) that power simulations
API Reference
Programmatic simulation submission and data export
Advanced Topics
Persona Chat
Chat with domain expert personas about your simulation results:- AGENT1 (Victoria): Corporate finance / regulatory expert
- AGENT2 (Dr. Raj): Aerospace / mission assurance engineer
- AGENT3 (Marcus): Legal tech startup founder
- AGENT4 (Dr. Kate): Wildlife ecology / RMEF researcher
API Mode
Submit simulations via REST API for cloud execution:API mode requires
TIMEPOINT_API_KEY in .env. See API Reference for authentication.Monitoring
Real-time monitoring during long simulations:- Current timepoint progress
- Entity state evolution
- Token/cost accumulation
- Estimated completion time
Summary
You’ve learned how to:- ✓ Run production templates
- ✓ Understand simulation output (JSON, JSONL, SQLite)
- ✓ Explore temporal modes (FORWARD, PORTAL, BRANCHING)
- ✓ Customize models, parallelism, budgets
- ✓ Export and analyze results
- ✓ Test convergence for reliability
Ready to dive deeper? Explore Temporal Modes to understand how PORTAL, BRANCHING, and CYCLICAL modes change causality semantics, or browse the Template Library for all 21 scenarios.

