用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill compute-normalization命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Strategy: Dialectic engine retuned for truth-seeking, not survival. A defender steelmans a claim into its MOST falsifiable form, a critic attacks to refute it, a judge classifies the exchange into BROKEN/CORROBORATED/UNFALSIFIABLE — the judge does NOT pick a winner or score persuasiveness. Methods: Irving debate (repurposed), Toulmin argumentation, Mayo severe testing.
Campaign: Logical extreme and boundary testing via reductio ad absurdum and edge-case analysis. Core question: Does this artifact collapse under logical limits and boundary conditions? Methods: Lakatos 1976, Dutilh Novaes 2016, BVA, Flyvbjerg Critical Case, Popper.
Campaign for mapping argument structures — extract claims, link evidence, assess strength, synthesize positions. Produces argument graphs in the wiki vault.
基于 SOC 职业分类
| name | compute-normalization |
| description | Normalize results by compute budget (Pareto analysis) |
| execution | subagent |
| prompt | ./prompt.md |
| input | method_scores, compute_costs |
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.
| 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} |
{
"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"}
}
}