literature-survey
Autonomous Literature Survey Campaign — 5 research paradigms (scoping, systematic, deep, narrative, snowball) with quantitative budget enforcement. Selects and executes the right survey paradigm based on research intent.
DeepseekModel
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质量 优秀 · 78
v1.0.0
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https://deepseekmodel.com/api/download.php?id=yogsoth-ai-de-anthropocentric-research-engine-skills-literature-survey-skill-md&format=skill
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标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
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name literature-survey description Autonomous Literature Survey Campaign — 5 research paradigms (scoping, systematic, deep, narrative, snowball) with quantitative budget enforcement. Selects and executes the right survey paradigm based on research intent. execution campaign dependencies {"strategies":["deep-survey","narrative-review","scoping-survey","snowball","systematic-survey"],"sops":["context-checkpoint","context-init"]} Literature Survey Autonomous literature survey engine. Five research paradigms, each a self-contained playbook with quantitative budget enforcement. You provide a research question — it searches, screens, reads, categorizes, identifies gaps, and produces a structured survey output. Strategy Routing Signal Strategy panoramic mapping of a new field, broad overview, field mapping → scoping-survey exhaustive coverage, PRISMA, systematic review → systematic-survey precise sub-question, specific mechanism, deep dive → deep-survey theory-driven, argument building, narrative → narrative-review seed papers, citation chain, lineage tracing → snowball Manifest Strategies (5) scoping-survey systematic-survey deep-survey narrative-review snowball Tactics (3) prisma-screening citation-chaining (shared: patent-mining, baseline-establishment) narrative-framing Subagent SOPs (11) survey-synthesis define-search-protocol categorize-papers (shared: meta-analysis, baseline-establishment) extract-data quality-assessment seed-selection saturation-detection (shared: patent-mining, benchmark-archaeology) taxonomy-mapping prisma-flowchart thematic-coding gap-identification (shared: benchmark-archaeology, baseline-establishment) Import SOPs (5, shared across all campaigns) web-search web-research paper-overview paper-search paper-research Budget Table Strategy web-search web-research paper-overview paper-search paper-research scoping-survey 100 10 100 20 0 systematic-survey 50 5 60 40 30 deep-survey 30 5 40 40 20 narrative-review 80 15 50 40 20 snowball 20 3 30 30 20 All values ±10% flexibility. Deviations require explicit reasoning. MCP Tools MCP Server Tools brave-search brave_web_search, brave_news_search, brave_llm_context apify rag-web-browser, google-scholar-scraper alphaxiv discover_papers, get_paper_content, answer_pdf_queries, read_files_from_github_repository semantic-scholar ss_paper, ss_paper_batch, ss_references, ss_citations, ss_recommendations, ss_relevance_search, ss_author, ss_author_papers Context Management Campaign start: context-init After each strategy completes: context-checkpoint (append to literature-survey context file) Available Strategies Optional, no fixed order; the final leaf is always a sop. Strategy When to use deep-survey Precise, targeted investigation of a specific sub-problem — few papers, all read in full depth. High paper-research ratio (50% deep-read rate). Use when the user knows exactly what they need to understand and requires detailed technical analysis with equations, hyperparameters, and specific claims extracted. narrative-review Theory-driven literature review for building arguments and frameworks. Flexible, subjective, and narrative-focused — selects evidence strategically to support a thesis. High web-research budget for blogs, opinion pieces, and industry perspectives. Use when the user is writing a position paper, survey introduction, or constructing a coherent narrative around a research theme. scoping-survey Broad landscape mapping strategy — quickly understand what exists in a field. Prioritizes breadth over depth with high paper-overview volume and minimal deep reading. Use when entering a new field or needing orientation before committing to deeper investigation. snowball Citation-chain-driven literature survey starting from seed papers. Traces research lineage in both forward (who cited this) and backward (what this cited) directions until saturation. High deep-read ratio (67%). Use when the user already has key papers and wants to find everything connected to them — ancestors, descendants, and branch points. systematic-survey Exhaustive PRISMA-style literature survey — comprehensive coverage of all related work on a specific question. Multi-stage screening, citation chaining, quality assessment, and structured data extraction. Use when the user needs to demonstrate complete literature coverage or conduct rigorous gap analysis. Available SOPs Optional, no fixed order; the final leaf is always a sop. SOP When to use context-checkpoint Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. context-init Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |