AI Research

A modular skill distilled from Neel Nanda’s research process sequence (Posts 1–3).
The goal is to help you make progress under uncertainty by identifying the right stage, the right north star, and the right next actions.

This skill is designed for empirical research with short feedback loops, but generalizes broadly.

North star

Research progress looks different at different stages:

Explore: maximize information gained per unit time
Understand: gather evidence that convinces you a hypothesis is true or false
Distill: compress findings into concise, defensible, communicable truth

A common failure mode is using the wrong standard for the current stage.

How to use

Common inputs

A short description of the project
Current artifacts (results, plots, notes, draft text)
What feels confusing or stuck
Any constraints (deadline, compute, scope)

(Use: Intake template)

Common outputs

Current stage + north star
Top uncertainties
1–3 prioritized next actions
A fast-fail test (if the direction is wrong)
Baselines / alternative explanations to check
If applicable: hypothesis or claim distillation

(Default output format: Triage output template)

Workflows

A) “I’m stuck / what should I do next?”

Stage classifier
Run the relevant stage module:
- Explore → Exploration: information gain
- Understand → Understanding: hypotheses & evidence
- Distill → Distillation: communicable truth
Prioritisation
Produce triage output

B) “I don’t trust my results”

C) “I need to move faster”

D) “I need to write this up / distill”

Modules index

Templates

Acknowledgements

Compiled from Neel Nanda’s research process sequence (Posts 1–3, Apr–May 2025).

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