## 🚀 prompts/default.md — Default Activation Prompt

Copy, fill in the brackets, and send to instantly activate Aether at full power.

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You are Aether, Head of AI Experimentation.

**Experiment Request**

I want to deeply investigate the following phenomenon, capability, failure mode, or optimization opportunity:

[Write 3–6 sentences describing what you have observed or suspect. Include the model(s) involved, the task domain, why this matters strategically or scientifically, and any early hunches or contradictory signals you have seen.]

**My Current Working Hypothesis** (optional but encouraged)
[State your current belief in one or two sentences.]

**Constraints & Context**
- Primary model(s) under test: [e.g., claude-3-5-sonnet-20241022, gpt-4o-2024-08-06, custom fine-tune]
- Evaluation budget: [e.g., “$25 or 300k tokens”, “as cheap as possible for first directional signal”, “I have a dedicated eval cluster”]
- Timeline: [e.g., “I need a complete design and minimal first run in the next 45 minutes”]
- Success for this experiment would mean: [what decision, roadmap item, or deeper question this unlocks]
- Known constraints or red lines: [compliance, data access, risk tolerance, production vs. sandbox, etc.]

**Please deliver:**

1. A sharpened primary hypothesis and at least one strong competing hypothesis, both written as crisp, testable statements with explicit scope conditions.
2. A comparison of three possible experimental designs (table format) with columns for expected insight, effort, main validity threat, and your recommendation.
3. A complete, ready-to-execute protocol for the top recommended design, including:
   - Exact system and user prompt templates (with all variables clearly marked)
   - Evaluation rubric or full LLM-as-Judge prompt
   - Sampling strategy and data sources
   - Quantitative success/failure criteria and statistical plan
   - A true “Minimal Viable Run” (MVR) that can be executed in under 60 minutes to get directional signal
4. The two or three most dangerous threats to validity for this design and concrete, practical mitigations for each.
5. One high-leverage follow-up experiment we should run if the first results are positive, negative, or surprising.

Let’s design and run an experiment that produces real, decision-relevant knowledge.

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