deeppapernote

name	deeppapernote
description	Generate a high-quality deep-reading note for a single paper and write it into an Obsidian-style vault. Use when the user gives a paper title, DOI, URL, arXiv ID, Zotero item, or local PDF and wants a polished Markdown note with strong structure, evidence-based analysis, and figure placeholders.

DeepPaperNote

Use this skill when the user wants one outcome:

• read one paper carefully
• generate a high-quality Markdown note
• save the note into an Obsidian-style vault when configured, or into the current workspace when no vault is configured

Chinese trigger examples:

• 给这篇论文生成深度笔记
• 写一篇高质量论文精读笔记
• 把这篇文章整理成 obsidian 笔记
• 读这篇论文并生成 md 笔记

This skill is intentionally narrow:

• it handles one paper at a time
• it does not update daily reading lists
• it does not treat a shallow abstract rewrite as a successful output
• it does not split the public entrypoint into separate setup, troubleshooting, or start commands

Core Standard

The finished note must be more than a summary. It should reconstruct the paper's argument:

• what problem it solves
• how the task is defined
• what data or materials it uses
• how the method or analysis actually works
• what results matter most
• what the paper does not prove
• why the paper is worth keeping

Default writer persona:

• a top-tier researcher or algorithm engineer
• writing a replication-oriented lab note
• not writing a popular-science explanation
• assuming the reader can follow Python, PyTorch, training loops, and evaluation logic

The note must adapt to the paper type. Use the same base structure, but shift emphasis for AI methods, benchmarks, clinical studies, and humanities or social-science papers.

Workflow

Follow this order:

1. resolve the paper identity
2. collect metadata
3. acquire the best available PDF
4. extract canonical raw source text: *_raw_sections.jsonl, *_source_manifest.json, and optional derived *_full_text.md
5. extract structural indexes and PDF assets
6. plan figure placement
7. build the full figure/table decision table
8. build the manifest synthesis bundle
9. have the model read the bundle plus raw sections and create a short JSON note_plan that satisfies the generated bundle contract
10. draft from the plan only after the grounding gate passes
11. have the model write the note
12. lint the final note against the same note_plan — this stage completes only when the lint artifact exists and every reported passes_* gate is true; otherwise revise and rerun lint. If the lint output contains passes_style_gate: false, apply the Style Gate Enforcement rule before advancing to step 13, 14, or 15
13. perform final_quality_review after lint passes
14. perform final_readability_review after the quality review passes
15. write into Obsidian

This is the required workflow for a normal single-paper note request, not a loose suggestion. Unless this skill explicitly marks a stage as optional, required stages must not be silently skipped, reordered into a shortcut, or treated as complete just because a partial artifact already exists.

Global no-short-circuit rule:

• do not stop after only the early stages and present the workflow as finished
• do not treat slowness, inconvenience, or temporary uncertainty as permission to bypass a required stage
• do not replace the declared workflow with an improvised shortcut
• if a required stage fails, only do one of three things:
  • retry that stage
  • enter a fallback that is explicitly allowed by this skill
  • stop and report which stage is blocked and which downstream required stages remain incomplete
• do not describe the whole task as complete while required downstream stages are still pending

Completion-language rule:

• say 笔记已完成 only when the required workflow is actually complete
• say 已生成草稿 when drafting is done but lint, final readability review, or save is still pending
• say 已通过校验 only when lint has actually been run and passed
• say 已保存到 Obsidian only when the write step has actually succeeded
• do not treat lint 已通过 as equivalent to 整篇笔记已经润色完成
• if final readability review is still pending, explicitly say the draft passed script lint but has not finished final language review
• if the workflow stopped early, name the current stage and the still-missing required stages instead of using completion language
• lint is a floor, not the writing objective

Core Execution Contract

SKILL.md plus the generated synthesis_bundle.json must be enough to complete a normal note-generation run. Files under references/ are optional stage-specific deep dives, not a default reading checklist.

Non-negotiable rules:

• evidence-first: draft from the synthesis bundle, source_manifest, raw sections, coverage metadata, explicit note_plan, and inspected paper evidence; never finish from title/abstract/headings alone
• raw-source authority: for ordinary PDFs, *_raw_sections.jsonl and *_source_manifest.json are the canonical reading material; old top-N evidence buckets, truncated section_texts, and candidate_chunks are not model-facing writing inputs
• fail-closed: if a usable PDF or sufficient evidence cannot be obtained after supported acquisition paths, stop and ask for better source material rather than producing a finished degraded note
• model-first: scripts structure evidence, but the model must decide emphasis, contribution, mechanism, limitations, and final Chinese prose
• required structure: include the canonical required sections, with 原文摘要翻译 before 一句话总结 and a dedicated 创新点 section immediately after 原文摘要翻译
• abstract translation: when abstract metadata exists, 原文摘要翻译 is a faithful Chinese translation of the original abstract, not a bilingual block and not the model's own summary
• mechanism depth: method, framework, and system papers should include ### 机制流程 under 方法主线, normally as a 3 to 4 step numbered flow with input, operation, and output destination
• placeholder-first figures: plan major figure/table placeholders first; replace one only when identity match and visual usability are both strong; otherwise keep the placeholder
• final quality gates: lint is a floor; after lint passes, first run final_quality_review for analytical depth, then run final_readability_review for language polish, and rerun lint if either review edits the note

Reference usage policy:

• do not load every reference file by default
• consult references/evidence-first.md, references/deep-analysis.md, or references/final-writing.md only when the paper is complex or the draft is too shallow
• consult references/figure-placement.md only for ambiguous figure/table placement or image replacement decisions
• consult references/obsidian-format.md only for Markdown, vault, frontmatter, or reference-link formatting details
• consult references/note-quality.md or references/paper-types.md only for final review or domain adaptation
• consult references/metadata-sources.md only when metadata is incomplete, and references/architecture.md only for repository maintenance decisions

Tool and Source Priority

Prefer the strongest available source in this order:

1. local PDF path given by the user
2. local Zotero item and local Zotero attachment if available
3. DOI and publisher metadata
4. arXiv or open-access PDF sources
5. Semantic Scholar or OpenAlex for metadata backfill

Before web resolution, use the bundled scripts/resolve_paper.py Zotero Local API path to check the desktop library. Its default --zotero-mode auto prefers a unique local match and falls back to the existing providers when Zotero is unavailable or has no match. An explicit Zotero key has no safe web fallback and must be verified locally. Use off to make no Local API request, or required when the reference must resolve through Zotero. A trusted JSON artifact or explicit local PDF remains authoritative and bypasses this lookup. A compatible session-scoped Zotero/MCP integration may still provide a trusted input artifact when available, but it is not required for the built-in path.

Local-library-first rule:

• search the local Zotero library first using the paper title, DOI, arXiv id, or exact Zotero item key
• If Zotero finds the paper, treat that result as the canonical identity resolution step.
• Prefer the safe local attachment path returned by the built-in Local API. If another compatible integration exposes only an attachment key and filename, use scripts/locate_zotero_attachment.py to find the PDF under the user's Zotero storage.
• If a local attachment path is available, pass it forward as the preferred PDF source.
• If no local attachment is found, still use the library-resolved metadata to avoid title ambiguity, then fall back to network PDF acquisition only for the file itself.
• If multiple local items are equally plausible, fail closed and request a DOI, arXiv id, or exact Zotero key rather than selecting one arbitrarily.
• Do not let a weaker title-only internet match override a confident local-library hit.

Output Rules

Formal Save states:

Save Target state	Required action
Vault configured or provided and usable	Perform the Formal Save to that vault.
Vault configured or provided, but the Formal Save fails	Keep the current Save Target and attempt an in-scope recovery. If it still cannot complete, report blocked; do not switch to workspace.
No vault configured or provided	Ask whether the user wants to provide one. Use workspace only after the user explicitly chooses not to use a vault.

• A normal note-generation request should complete in one pass: note text, figure placeholder decisions, image materialization when confident, and final save.
• Do not stop after a text-only draft just to ask whether the user wants figures inserted. Finish the figure replacement decision inside the same task unless the user explicitly asked for text only.
• The note must use real heading levels: #, ##, and ###.
• Every final note must start with an Obsidian YAML properties block above the # title heading. Include at least a tags field with a papers/<domain> value and useful aliases; include date, doi, or arxiv_id when known, and omit unavailable fields rather than inventing placeholders.
• ## 核心信息 must be a fixed metadata block only. Use only these fields, in this order, as - 字段名: 值 bullets: 标题, 标题翻译, 作者, 机构, 发表时间, 发表渠道, DOI, arXiv, 论文链接, 代码 / 项目, 数据 / 资源, 论文类型. Omit unavailable fields; put any guide sentence, takeaway, or analysis in 一句话总结 or a later section instead.
• The note should include 原文摘要翻译 near the beginning when abstract metadata is available, before 一句话总结.
• When abstract metadata is available, 原文摘要翻译 should directly translate the original paper abstract into Chinese rather than restating it as your own summary.
• The 原文摘要翻译 section itself should be Chinese-only; do not place English abstract sentences or English paragraph excerpts in that section.
• Do not mix later judgments, innovation summaries, or hindsight explanations into 原文摘要翻译; keep it as the original abstract translated into Chinese.
• The note should include a dedicated 创新点 section immediately after 原文摘要翻译 and before 一句话总结.
• The 创新点 section should not be empty praise. It should enumerate the paper's actual innovations and briefly explain why each one matters.
• High-quality notes should usually contain multiple meaningful subheadings in the technical sections when the paper is non-trivial.

Model-first rule:

• scripts may gather and structure evidence
• scripts must not be the primary mechanism for understanding the paper
• final paper understanding and note writing belong to the model
• use the generated bundle contract to choose the paper type, section semantics, evidence-backed claims, boundaries, comparisons, and reusable follow-up questions; script suggestions remain hints rather than writing authority
• do not require or expose a long free-form <thinking> block
• for technical papers, prefer replication-grade explanation over high-level summary
• if formulas, objectives, or complexity expressions are central, include the key ones in the final note
• render math as $...$ or $$...$$, not as inline code or fenced code blocks
• before final save, explicitly self-review whether the note contains enough technical detail, key numbers, and any necessary formulas
• during final_quality_review, check the full note against seven questions: whether the central evidence chain is complete, whether key settings and numbers are present, whether mechanisms or protocols are mapped to the result pattern they explain, whether the paper is positioned against strong baselines or alternative routes, whether Discussion/Limitations conclusions are explained mechanistically, whether proven claims are separated from unproven claims, and whether the research, engineering, replication, or validity takeaways are specific enough to reuse
• central quantitative comparisons with three or more systems, settings, tasks, datasets, metrics, or ablation rows should normally be written as compact Markdown tables, followed by interpretation; do not leave the main result table as a loose bullet list when a table would be clearer
• short papers still need a complete deep note: use the saved space to explain protocol details, ablations, limitations, and deployment or replication implications rather than compressing the note into a terse summary
• after final_quality_review passes, reread the full note once more for readability; do not stop at formal compliance only
• in final_readability_review, ordinary English phrase leftovers should usually be rewritten into natural Chinese, while stable proper nouns may remain in English
• do not use final_readability_review to invent new facts, empty filler text, or shallower but safer wording just to satisfy lint

The topic references above can improve difficult runs, but the normal execution path should not depend on reading all of them.

Scripts

Use these bundled scripts rather than rebuilding the workflow from scratch:

• scripts/check_environment.py
• scripts/create_input_record.py
• scripts/locate_zotero_attachment.py
• scripts/resolve_paper.py
• scripts/run_pipeline.py
• scripts/collect_metadata.py
• scripts/fetch_pdf.py
• scripts/extract_source_text.py
• scripts/extract_evidence.py
• scripts/extract_pdf_assets.py
• scripts/plan_figures.py
• scripts/plan_figure_table_decisions.py
• scripts/build_synthesis_bundle.py
• scripts/lint_grounding.py
• scripts/lint_note.py
• scripts/materialize_figure_asset.py
• scripts/write_obsidian_note.py

Python interpreter rule:

• DeepPaperNote requires Python >=3.10.
• Before running repository scripts, check the interpreter version instead of assuming the current shell default is compatible.
• If the default python3 is below 3.10, automatically look for another available interpreter that satisfies the requirement, such as python3.12, python3.11, python3.10, /opt/anaconda3/bin/python3, /opt/homebrew/bin/python3, or /usr/local/bin/python3.
• Use the first compatible interpreter you find and continue with that interpreter for the repository scripts in the current task.
• If no compatible interpreter is available, stop and clearly tell the user which interpreter was found, which version it reported, and that DeepPaperNote requires Python >=3.10.

Troubleshooting rule:

• use scripts/check_environment.py only when a concrete dependency or integration question is blocking execution
• explain required dependencies, optional enhancements, and downgrade behavior directly rather than redirecting the skill into a separate troubleshooting workflow
• do not feature environment inspection as a public pseudo-command surface

Current status:

• the single-paper deterministic core pipeline is implemented as an MVP
• scripts/run_pipeline.py now defaults to building a model-facing synthesis bundle
• scripts/write_obsidian_note.py can write the final note into a target vault
• patch the scripts rather than replacing the workflow ad hoc

Limits

• If the paper identity is ambiguous, confirm before writing.
• If the PDF is unavailable after all supported acquisition paths have been tried, stop and report what input is needed; do not produce a degraded, provisional, or abstract-only note as the finished output. Supported acquisition paths include local PDF, Zotero attachment, metadata pdf_url, direct PDF URL, arXiv/open-access sources, publisher PDF if accessible, DOI enrichment, and any other current fetch path implemented by the workflow.
• Placeholder-first figure planning is required; image extraction is optional and must never reduce textual coverage.