/compute-normalization
Normalize results by compute budget (Pareto analysis)
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill compute-normalization --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/compute-normalization
Context preview
The summary Claude sees to decide when to auto-load this skill.
Normalize results by compute budget (Pareto analysis)
SKILL.md
compute-normalization.SKILL.mdname: compute-normalization
description: Normalize results by compute budget (Pareto analysis)
execution: subagent
prompt: ./prompt.md
input: method_scores, compute_costs
Compute Normalization
Purpose
Analyze the performance-compute tradeoff across methods. Identify Pareto-optimal methods (best performance for a given compute budget), compute-normalized rankings, and efficiency frontiers. Essential for practical method selection under resource constraints.
Input Schema
| Field | Type | Description | |-------|------|-------------| | method_scores | object[] | Array of {method, dataset, metric, score} | | compute_costs | object[] | Array of {method, flops, gpu_hours, params, training_cost_usd} |
Output Schema
{
"pareto_frontier": [
{
"method": "string",
"score": 0.0,
"compute_metric": "string",
"compute_value": 0.0,
"is_pareto_optimal": true
}
],
"efficiency_rankings": [
{
"method": "string",
"score_per_flop": 0.0,
"score_per_gpu_hour": 0.0,
"score_per_param": 0.0
}
],
"compute_normalized_scores": [
{
"method": "string",
"raw_score": 0.0,
"normalized_score": 0.0,
"normalization_method": "string"
}
],
"practical_recommendations": {
"budget_low": {"method": "string", "score": 0.0, "cost": "string"},
"budget_medium": {"method": "string", "score": 0.0, "cost": "string"},
"budget_high": {"method": "string", "score": 0.0, "cost": "string"}
}
}Read more
name: compute-normalization description: Normalize results by compute budget (Pareto analysis) execution: subagent prompt: ./prompt.md input: method_scores, compute_costs
Compute Normalization
Purpose
Analyze the performance-compute tradeoff across methods. Identify Pareto-optimal methods (best performance for a given compute budget), compute-normalized rankings, and efficiency frontiers. Essential for practical method selection under resource constraints.
Input Schema
| Field | Type | Description | |-------|------|-------------| | method_scores | object[] | Array of {method, dataset, metric, score} | | compute_costs | object[] | Array of {method, flops, gpu_hours, params, training_cost_usd} |
Output Schema
{
"pareto_frontier": [
{
"method": "string",
"score": 0.0,
"compute_metric": "string",
"compute_value": 0.0,
"is_pareto_optimal": true
}
],
"efficiency_rankings": [
{
"method": "string",
"score_per_flop": 0.0,
"score_per_gpu_hour": 0.0,
"score_per_param": 0.0
}
],
"compute_normalized_scores": [
{
"method": "string",
"raw_score": 0.0,
"normalized_score": 0.0,
"normalization_method": "string"
}
],
"practical_recommendations": {
"budget_low": {"method": "string", "score": 0.0, "cost": "string"},
"budget_medium": {"method": "string", "score": 0.0, "cost": "string"},
"budget_high": {"method": "string", "score": 0.0, "cost": "string"}
}
}The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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