maxharar/paiarchitecture/science

Customization

Before executing, check for user customizations at: ~/.claude/skills/PAI/USER/SKILLCUSTOMIZATIONS/Science/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

1. Send voice notification:

``bash curl -s -X POST http://localhost:8888/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the Science skill to ACTION"}' \ > /dev/null 2>&1 & ``

1. Output text notification:

`` Running the **WorkflowName** workflow in the **Science** skill to ACTION... ``

This is not optional. Execute this curl command immediately upon skill invocation.

Science - The Universal Algorithm

The scientific method applied to everything. The meta-skill that governs all other skills.

The Universal Cycle

GOAL -----> What does success look like?
   |
OBSERVE --> What is the current state?
   |
HYPOTHESIZE -> What might work? (Generate MULTIPLE)
   |
EXPERIMENT -> Design and run the test
   |
MEASURE --> What happened? (Data collection)
   |
ANALYZE --> How does it compare to the goal?
   |
ITERATE --> Adjust hypothesis and repeat
   |
   +------> Back to HYPOTHESIZE

The goal is CRITICAL. Without clear success criteria, you cannot judge results.

Workflow Routing

Output when executing: Running the **WorkflowName** workflow in the **Science** skill to ACTION...

Core Workflows

Diagnostic Workflows

Resource Index

Domain Applications

Scale of Application

Integration Points

Key Principles (Quick Reference)

1. Goal-First - Define success before starting
2. Hypothesis Plurality - NEVER just one idea (minimum 3)
3. Minimum Viable Experiments - Smallest test that teaches
4. Falsifiability - Experiments must be able to fail
5. Measure What Matters - Only goal-relevant data
6. Honest Analysis - Compare to goal, not expectations
7. Rapid Iteration - Cycle speed > perfect experiments

Anti-Patterns

Quick Start

1. Goal - What does success look like?
2. Observe - What do we know?
3. Hypothesize - At least 3 ideas
4. Experiment - Minimum viable tests
5. Measure - Collect goal-relevant data
6. Analyze - Compare to success criteria
7. Iterate - Adjust and repeat

The answer emerges from the cycle, not from guessing.