compute-normalization
Normalizes research results against compute costs. Helps determine efficiency frontiers and select the best methods for a given budget.
Install
mkdir -p .claude/skills/compute-normalization && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11215" && unzip -o skill.zip -d .claude/skills/compute-normalization && rm skill.zipInstalls to .claude/skills/compute-normalization
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Normalize results by compute budget (Pareto analysis)Key capabilities
- →Analyze performance vs compute cost
- →Identify Pareto-optimal methods
- →Generate efficiency rankings
How it works
It performs Pareto analysis on performance versus compute metrics to identify the most efficient methods for resource-constrained deployments.
Inputs & outputs
When to use compute-normalization
- →Analyze performance vs FLOPs
- →Compare model training costs
- →Identify Pareto-optimal methods
- →Normalize experiment results
About this skill
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"}
}
}
When not to use it
- →When compute cost is irrelevant
- →When performance metrics are not available
Limitations
- →Requires parseable performance and cost data
How it compares
It provides normalized efficiency rankings rather than just raw performance scores.
Compared to similar skills
compute-normalization side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| compute-normalization (this skill) | 0 | 1mo | No flags | Intermediate |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 2mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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