amogus/config/prompts/reflection.md
Antigravity 071906df59 feat: Complete LLM agent framework with fog-of-war, meeting flow, and prompt assembly
- Core engine: simulator, game mechanics, triggers (138 tests)
- Fog-of-war per-player state tracking
- Meeting flow: interrupt, discussion, voting, consolidation
- Prompt assembler with strategy injection tiers
- LLM client with fallbacks for models without JSON/system support
- Prompt templates: action, discussion, voting, reflection
- Full integration in main.py orchestrator
- Verified working with free OpenRouter models (Gemma)
2026-02-01 00:00:34 -05:00

1.9 KiB

Among Us — Reflection Phase Prompt

The game has ended. Take this opportunity to learn from what happened.

What to Reflect On

If You Won

  • What strategies worked well?
  • What decisions led to victory?
  • How did you read other players correctly?
  • What would you do the same way?

If You Lost

  • What mistakes did you make?
  • Where did your reasoning go wrong?
  • What signs did you miss?
  • What would you do differently?

General Observations

  • Which players were most deceptive?
  • Which players played honestly?
  • What patterns did you notice?
  • What new strategies did you observe?

Your Learned Memory

Your learned scratchpad persists across games. Use it to remember:

  1. Strategies: Approaches that work or fail
  2. Player patterns: If you play with the same players again
  3. Meta-observations: How LLMs tend to play
  4. Mistakes: Things to avoid in future games

Output Format

Respond with valid JSON:

{
  "edits": {
    "learned": "New lessons: [Your insights here]"
  },
  "done": true
}

Iterative Editing

Set "done": false if you want another pass to refine your thoughts:

{
  "edits": {
    "learned": "Draft thoughts..."
  },
  "done": false
}

You'll get another chance to edit until you set "done": true.

Example Learned Content

## Strategies That Work
- As impostor: sabotage lights before killing in electrical
- As crewmate: always check admin table when passing

## Mistakes to Avoid
- Don't accuse without evidence (looks sus when wrong)
- Don't follow same player too long (looks like stalking)

## Player Patterns
- Aggressive accusers are often impostors deflecting
- Quiet players who suddenly speak often have real info

## Meta Observations
- Stack kills are hard to witness, stay spread out
- First meeting accusations rarely lead to correct ejections

Learn well. Play better next time.