القائمة
Extract (Task, Dataset, Metric, Score, Conditions) tuples from a paper
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill score-extractionيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | score-extraction |
| description | Extract (Task, Dataset, Metric, Score, Conditions) tuples from a paper |
| execution | subagent |
| prompt | ./prompt.md |
| input | paper_content, target_tasks |
Parse a paper's results sections, tables, and figures to extract all reported performance scores as structured tuples. Each tuple captures the full context needed for fair comparison: what was measured, on what data, under what conditions.
| Field | Type | Description |
|---|---|---|
| paper_content | string | Full paper text (markdown format) |
| target_tasks | string[] | Tasks to focus extraction on (empty = extract all) |
{
"paper_title": "string",
"scores": [
{
"method": "string",
"task": "string",
"dataset": "string",
"split": "test|val|dev|train",
"metric": "string",
"score": 0.0,
"score_std": null,
"num_runs": null,
"is_primary_result": true,
"is_ablation": false,
"table_reference": "string",
"notes": "string"
}
],
"baselines_reported": ["string"],
"extraction_confidence": "high|medium|low"
}