zotero-taxonomy-curator
Domain-configurable Zotero-to-Obsidian literature curation and paper close-reading skill. Use when the user asks to 精读、文献精读、论文精读、生成精读笔记, analyze/read a paper, or process a Zotero item/collection/title/DOI/PDF into a structured Obsidian literature note. It extracts Zotero evidence, writes Chinese close-reading notes, maps original tags to a controlled taxonomy, initializes local config and empty taxonomy, handles review/survey papers with review_scope_tags, writes model and formula sections with LaTeX evidence discipline, and updates literature indexes for any research field.
DeepseekModel
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质量 优秀 · 78
v1.0.0
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name zotero-taxonomy-curator description Domain-configurable Zotero-to-Obsidian literature curation and paper close-reading skill. Use when the user asks to 精读、文献精读、论文精读、生成精读笔记, analyze/read a paper, or process a Zotero item/collection/title/DOI/PDF into a structured Obsidian literature note. It extracts Zotero evidence, writes Chinese close-reading notes, maps original tags to a controlled taxonomy, initializes local config and empty taxonomy, handles review/survey papers with review_scope_tags, writes model and formula sections with LaTeX evidence discipline, and updates literature indexes for any research field. Zotero Taxonomy Curator Use this skill when a user wants to turn Zotero items, collections, annotations, manual tags, PDF text, or notes into structured Obsidian literature notes backed by a user-defined taxonomy. Core idea This skill is taxonomy-first. It does not treat paper summarization as the final goal. Its goal is to help the user build a reusable literature system: Zotero evidence -> controlled taxonomy -> structured note -> literature index Source policy Use sources in this order: User-provided manuscript notes, Zotero exports, PDFs, annotations, tables, or data files. Local Zotero database paths from config/local.yaml . Taxonomy rules from config/taxonomy.yaml . The bundled note template in templates/literature-note-template.md inside this skill folder, or a project-level override supplied by the user. Never invent paper conclusions, numerical results, page numbers, formulas, authors, DOI, dataset names, or Zotero keys. If evidence is missing, say so. Review, model, and formula rules Review papers Detect review papers before assigning tags. Review signals include review , survey , systematic literature review , bibliometric , scoping review , taxonomy , classification , meta-analysis , state-of-the-art , or a paper whose main contribution is organizing a field rather than proposing a new model or experiment. For review papers: set paper_type: review ; fill review_type when possible, such as narrative-review , systematic-review , bibliometric-review , scoping-review , taxonomy-review , meta-analysis , tutorial-review , or hybrid-review ; keep canonical_tags narrow: use only the review type and its core scope; use review_scope_tags for covered but non-core concepts; these mean "covered by this review", not "proposed by this paper"; do not promote every method, constraint, objective, dataset, or application mentioned in a review table to canonical_tags ; write the note around review positioning, classification framework, research trajectory, method families, gaps, opportunities, and key reference clusters. Models and formulas For modeling, optimization, algorithm, learning, or quantitative papers: separate assumptions, sets, indices, parameters, variables, objective terms, constraints, algorithm steps, and experimental findings; use LaTeX for sets, parameters, variables, objectives, constraints, state transitions, value functions, loss functions, probabilities, and expectations; preserve the paper's notation unless it is inconsistent or undefined; do not invent objective terms, constraints, variables, formulas, solver settings, or proof claims; if a formula is corrupted by PDF extraction, write that the original PDF must be checked instead of reconstructing a formula from guesswork; after each important formula or mechanism, explain what it does, key symbols, where it sits in the method, and why it is reusable. Literature-review reuse When extracting material for a future literature review: record only claims supported by local evidence from the note, PDF, annotation, or Zotero metadata; distinguish background claims, research gaps, method comparisons, experiment evidence, and limitations; avoid inflated claims such as "proves", "always", or "best" unless the paper itself and the evidence justify them; do not create citations, DOIs, page numbers, or result values that were not present in the source evidence. Configuration files Expected public templates: config/local.example.yaml config/taxonomy.example.yaml project-level templates/literature-note-template.md , if the user wants to override the bundled skill template Expected private local files: config/local.yaml config/taxonomy.yaml Do not require hard-coded absolute paths. If config/local.yaml is absent, ask the user to run scripts/init_curator_project.py or create it from config/local.example.yaml . First-run initialization When a user has just installed the skill or the current project has no config/local.yaml , initialize the project before extracting papers: python <skill-dir>/scripts/init_curator_project.py The initializer creates: config/local.yaml ; config/taxonomy.yaml as an empty taxonomy starter; templates/literature-note-template.md ; the note output folder, by default notes/literature/ ; a starter Literature Index.md . It tries to detect Zotero paths from environment variables, Zotero profile preferences, and common default locations. If detection fails, rerun it with --zotero-sqlite and --zotero-storage , or edit config/local.yaml . Workflow 1. Evidence Collector: build an evidence packet For each Zotero item, collect: title, authors, year, journal, DOI, URL; Zotero item key; attachment and PDF key; resolved PDF path when available; Zotero collection paths; original Zotero tags; notes, annotations, and highlights; full-text cache excerpts; PDF page evidence when needed. At this stage, do not translate, summarize, or write final notes. If deterministic extraction is needed, run scripts/extract_zotero_evidence.py . 2. Domain Card Builder: describe the user's field Before writing the note, identify the user's taxonomy categories. Common categories include: document type; research topic; theory or framework; method family; data and evidence; contribution type. Domain projects may replace these categories with field-specific ones. 3. Taxonomy Mapper: map tags Map Zotero tags and evidence-derived concepts to the taxonomy: preserve original Zotero tags and the tag-selection basis in the note body under 标签标记 , not as noisy frontmatter fields; map stable concepts to canonical_tags ; place uncertain or emerging concepts in candidate_tags ; avoid adding fine-grained tags unless they improve retrieval or comparison; keep detailed category definitions in config/taxonomy.yaml rather than duplicating the full taxonomy tree in every note; use aliases in the taxonomy when Zotero tag spelling differs from canonical style. 4. Note Composer: write the literature note Use the configured template and include: basic information; lightweight tag marking; core summary; problem positioning; review-specific sections when paper_type: review ; model or assumptions; solution or analytical method; core formulas or mechanisms when applicable; evidence, data, and experiments; main findings; literature-review reusable points; the user's assessment, reuse value, and follow-up questions. 5. Index Keeper: maintain the literature index If the user has an index file, update it with: year; wiki link; title; Zotero item key; DOI; main tags; one-sentence positioning. 6. Taxonomy Gatekeeper: validate Before reporting completion: read the note back from disk; check frontmatter; check that canonical tags exist in the taxonomy; check candidate tags against candidate_terms when applicable; for review papers, check that review_scope_tags is not being used as a substitute for ordinary canonical_tags ; check that formulas use LaTeX and that unclear formulas are marked for PDF verification rather than guessed; check for encoding problems; check that local temporary files are not treated as final output. For deterministic tag checks, run scripts/validate_taxonomy_notes.py . Taxonomy governance rules Use the smallest stable tag set that supports the user's research workflow. Promote a candidate term to canonical only when: it appears across multiple sources or is established in the field; it cannot be adequately covered by an existing broader tag; it improves retrieval, comparison, or review writing; it has explicit evidence. Do not promote: one-off acronyms; local operator names; city names unless the field uses them as stable study areas; author names; funding numbers; journal names; isolated background phrases. Output format For a single item: Completed - Zotero item: - Output note: - Taxonomy: - Index update: Tag decisions - Canonical: - Candidate: - Not mapped: Risks / missing evidence - ... For a collection: Collection summary - Items scanned: - Already processed: - New notes: - Skipped: - Failed: Taxonomy changes - New candidates: - New canonical tags: - Merge suggestions:
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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) |