القائمة
Medium-depth literature search — read AI-summarized reports for every paper analyzed
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 literature-searchيبقى 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 | literature-search |
| description | Medium-depth literature search — read AI-summarized reports for every paper analyzed |
| type | sop |
| layer | sop |
| agents | ["alphaxiv","semantic-scholar"] |
| tools | {"alphaxiv":["discover_papers","get_paper_content"],"semantic-scholar":["relevanceSearch","paper","paperBatch","citations","references"]} |
| input | query (string), scope (survey | gap-analysis | background) |
| output | PaperAnalysis[] with metadata + AI summary content |
| dependencies | {"sops":["literature-research"]} |
Medium-depth reading. Understand methods, contributions, and findings via AI-generated summary reports. Suitable for literature surveys, gap analysis, and building background knowledge.
Use this when you need to:
This skill reads AI-summarized reports — not raw full text. For rigorous analysis requiring raw text, use literature-research.
| Tool | Purpose | Returns |
|---|---|---|
alphaxiv.discover_papers | Primary search — arXiv semantic search | Ranked paper list with metadata |
ss.relevanceSearch | Supplementary search — non-arXiv papers | Title, abstract, authors, citationCount |
ss.paper / ss.paperBatch | Metadata enrichment | Citation count, DOI, S2 ID, externalIds |
ss.citations | Papers that cite this paper (incoming) | Citing paper list with context |
ss.references | Papers this paper cites (outgoing) | Referenced paper list |
alphaxiv.get_paper_content | AI summary report (fullText: false) | Structured AI-generated paper report |
For EVERY paper selected for analysis, you MUST call:
alphaxiv.get_paper_content(url: arxiv_url, fullText: false)
This returns an AI-generated summary report optimized for LLM consumption.
PROHIBITED:
REQUIRED:
Primary (arXiv):
alphaxiv.discover_papers(
keywords: ["keyword1", "keyword2", "keyword3"],
question: "Detailed description of papers needed",
difficulty: 5
)
Supplementary (non-arXiv):
ss.relevanceSearch(
query: "search terms",
limit: 20,
year: "2022-2024"
)
For papers found via alphaxiv, enrich with citation data:
ss.paperBatch(
paper_ids: ["ARXIV:2301.xxxxx", "ARXIV:2302.xxxxx", ...]
)
Returns: citationCount, DOI, S2 ID for each paper.
Choose top N papers (typically 5-15) based on:
For each selected paper:
alphaxiv.get_paper_content(
url: "https://arxiv.org/abs/XXXX.XXXXX",
fullText: false
)
fullText: false (default) returns an AI-generated intermediate report:
To find related work not caught by keyword search:
ss.citations(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
ss.references(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
Filter results by year and citation count, then repeat Steps 3-4 for promising papers.
fullText: false (default) — AI-generated report, faster, structuredfullText: true — raw extracted text, slower, complete (use in literature-research, not here)https://arxiv.org/abs/XXXX.XXXXX), PDF URL, alphaXiv URL2301.12345 → ARXIV:2301.12345)offset and limit for pagination# Step 1: Search
alphaxiv.discover_papers(
keywords: ["vision transformer", "attention", "ViT"],
question: "Papers proposing or analyzing attention mechanisms in vision transformers",
difficulty: 5
)
ss.relevanceSearch(query: "vision transformer attention mechanism", limit: 15, year: "2022-2024")
# Step 2: Enrich
ss.paperBatch(paper_ids: ["ARXIV:2010.11929", "ARXIV:2103.14030", ...])
# Step 3: Select top 8 by citation count + relevance
# Step 4: Read each
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2010.11929") # ViT
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2103.14030") # Swin
# ... repeat for all 8
# Step 5: Expand via citations of ViT
ss.citations(paper_id: "ARXIV:2010.11929", limit: 30)
# Step 1: Broad search
alphaxiv.discover_papers(
keywords: ["LLM", "efficient inference", "quantization", "pruning"],
question: "Methods for making large language model inference faster or cheaper",
difficulty: 6
)
# Step 2-4: Enrich, select 10, read AI summaries
# Step 5: Check what recent papers cite the seminal works
ss.citations(paper_id: "ARXIV:2210.17323", limit: 50) # GPTQ
ss.citations(paper_id: "ARXIV:2306.00978", limit: 50) # AWQ
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| literature-research | Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis |