Add DeepSeek QQwrite brief workflow
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SKILL.md
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SKILL.md
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@ -16,8 +16,8 @@ It can also route through the user's Zotero/Obsidian literature-note workflow:
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extract keywords from the manuscript topic or draft, use QQnote-generated
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Zotero child notes or paper tables as literature inputs, ask DeepSeek to screen
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those candidate notes into strongly related and weakly related papers under
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QQsci's rules, then use those papers to improve positioning, introduction
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logic, comparison, novelty framing, and citations.
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QQsci's rules, ask DeepSeek to draft a structured QQwrite brief from the
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screened papers, then have QQsci check the brief before passing it to QQwrite.
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## Core stance
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@ -46,6 +46,7 @@ logic, comparison, novelty framing, and citations.
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| [references/comparative-review-audit.md](references/comparative-review-audit.md) | User wants a Nature-response-like pre-review based on comparison with strongly related papers, including missing experiments, format, figures, spelling, and terminology consistency |
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| [references/submission-package-audit.md](references/submission-package-audit.md) | User provides or mentions a submission package with manuscript, Supporting Information, highlights, cover letter, figures, SI figures, TOC, graphical abstract, or related Word/PPT files |
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| [references/literature-routing.md](references/literature-routing.md) | User wants Zotero/Obsidian/DeepSeek note search, strong/weak related-paper screening, keyword extraction, or literature-grounded rewriting |
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| [references/qqwrite-brief.md](references/qqwrite-brief.md) | DeepSeek screening results need to be converted into a QQwrite writing brief or checked before QQwrite consumes them |
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| [references/word-citation-comments.md](references/word-citation-comments.md) | User wants citation insertion guidance in Word, reference placement, DOI-only comments, or annotated `.docx` citation suggestions |
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| [references/zotero-obsidian-subskill.md](references/zotero-obsidian-subskill.md) | User asks QQsci to obtain or refresh literature-note inputs through QQnote, including Zotero child-note summaries or paper comparison tables |
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| [references/configuration.md](references/configuration.md) | You need DeepSeek, Zotero, Obsidian, QQnote inheritance, config precedence, secret handling, or wrapper-script usage |
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@ -133,14 +134,19 @@ DeepSeek/AwesomeGPT Zotero notes generated by QQnote or equivalent sources.
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- `weakly related`: same direction, adjacent material family, same
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characterization logic, similar device architecture, or useful writing
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pattern.
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5. Use strongly related papers for novelty, gap, introduction, benchmark, and
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5. Ask DeepSeek to generate a `qqwrite_brief` from the screened papers when the
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next step is QQwrite drafting or template-driven `.docx` generation. Open
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`references/qqwrite-brief.md` for the schema and checks.
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6. QQsci checks the brief for required fields, DOI preservation, obvious
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overclaims, weak-paper misuse, and missing author evidence before handoff.
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7. Use strongly related papers for novelty, gap, introduction, benchmark, and
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citation placement.
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6. Use weakly related papers for broader field framing, mechanism language,
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8. Use weakly related papers for broader field framing, mechanism language,
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section architecture, and cautious comparison.
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7. For Word manuscript citation suggestions, open
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9. For Word manuscript citation suggestions, open
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`references/word-citation-comments.md` and add native Word comments that
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contain DOI strings only.
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8. Return a source map before or with the manuscript revision. Never cite a
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10. Return a source map before or with the manuscript revision. Never cite a
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paper only because the title seems similar.
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## Comparative pre-review workflow
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@ -127,8 +127,9 @@ QQsci and then QQwrite:
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}
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```
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If the output will feed QQwrite, QQsci should convert the screened papers into a
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`qqwrite_brief`-style section plan rather than passing raw notes directly.
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If the output will feed QQwrite, ask DeepSeek to generate the `qqwrite_brief`
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draft from the screened papers, then let QQsci validate and clean the brief
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before QQwrite consumes it. See `qqwrite-brief.md`.
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## Strong vs weak relation rules
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@ -0,0 +1,122 @@
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# QQwrite Brief
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Use this file when DeepSeek screening results need to become an input package
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for QQwrite.
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## Ownership
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DeepSeek may generate the `qqwrite_brief` draft from:
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- the manuscript profile
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- author evidence and figure notes
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- QQnote/Zotero/Obsidian note candidates
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- DeepSeek-screened strong/weak related papers
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- target journal and template choice
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QQsci owns:
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- the brief schema
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- the prompt that asks DeepSeek to generate the brief
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- schema validation
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- obvious overclaim and DOI-preservation checks
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- handoff to QQwrite
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QQwrite consumes the brief. QQwrite should not re-screen literature or decide
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which papers are strong/weak.
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## Required Schema
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Ask DeepSeek to return JSON whenever possible:
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```json
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{
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"target_journal": "",
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"article_type": "",
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"recommended_word_template": "afm/article-template.docx",
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"material_system": "",
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"application": "",
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"central_claim": "",
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"novelty_gap": "",
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"design_principle": "",
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"key_author_evidence": [
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{
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"evidence": "",
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"source": "manuscript / figure / SI / author note",
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"supports": ""
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}
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],
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"strong_related_papers": [
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{
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"doi": "",
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"title": "",
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"usable_for": [],
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"must_cite_near": [],
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"reason": ""
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}
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],
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"weak_related_papers": [
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{
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"doi": "",
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"title": "",
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"usable_for": [],
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"reason": ""
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}
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],
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"section_plan": {
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"Title": [],
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"Abstract": [],
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"Introduction": [],
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"Results and Discussion": [],
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"Conclusion": [],
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"Experimental Section": []
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},
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"claim_reference_map": [
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{
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"claim": "",
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"author_evidence": "",
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"supporting_dois": [],
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"writing_location": "",
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"status": "supported / needs author evidence / citation only / do not claim"
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}
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],
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"must_not_overclaim": [],
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"missing_inputs": [],
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"qqwrite_instructions": []
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}
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```
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## DeepSeek Prompt
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Give DeepSeek the screened-paper JSON and ask:
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```text
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Using the screened strong/weak related papers and the author-provided manuscript
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evidence, generate a QQwrite writing brief.
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Rules:
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1. Do not invent experimental results, mechanisms, references, DOI values, or
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figure numbers.
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2. Strong papers may define novelty, direct comparison, benchmark, and citation
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placement.
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3. Weak papers may support field framing, mechanism language, or writing
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structure, but must not be treated as direct benchmark papers.
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4. Every major claim must include author evidence, supporting DOI, or a clear
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status explaining that it still needs evidence.
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5. Preserve DOI strings exactly as provided.
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6. Return JSON only, following the QQwrite brief schema.
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```
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## QQsci Check Before Handoff
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Before passing the brief to QQwrite, check:
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- required top-level fields are present
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- `central_claim` is backed by author evidence
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- every DOI came from the screened output or user input
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- weak papers are not used as direct benchmark unless justified
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- `must_not_overclaim` and `missing_inputs` are not empty when evidence is
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incomplete
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- `recommended_word_template` points to a QQwrite-owned template
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If the brief fails checks, mark the issue in `missing_inputs` or ask DeepSeek to
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repair only the failed fields. Do not let QQwrite consume a malformed brief.
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@ -32,7 +32,10 @@ Recommended handoff:
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1. QQsci extracts claims, evidence, related papers, target-journal positioning,
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and package risks.
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2. QQwrite uses the selected template and QQsci's structured outputs to draft or
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format the manuscript.
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3. QQsci audits the resulting package again against figures, SI, and strong
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2. DeepSeek generates a `qqwrite_brief` draft from screened papers and author
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evidence.
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3. QQsci validates the brief and passes it to QQwrite.
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4. QQwrite uses the selected template and checked brief to draft or format the
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manuscript.
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5. QQsci audits the resulting package again against figures, SI, and strong
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related papers.
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@ -94,6 +94,46 @@ Output JSON keys:
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"""
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def build_brief_prompt(profile: dict[str, object]) -> str:
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return f"""After screening the literature notes, generate a QQwrite writing brief for this materials-science manuscript.
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Manuscript profile:
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- Material/system terms: {', '.join(profile.get('exact_or_chemical_terms', [])) or 'xxx'}
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- Material family: {', '.join(profile.get('material_family_hints', [])) or 'xxx'}
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- Device/application: {', '.join(profile.get('device_application_hints', [])) or 'xxx'}
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- Target journal or tier: {profile.get('target_journal') or 'xxx'}
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- Extra author notes: {profile.get('extra_notes') or 'xxx'}
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Use the screened strongly_related_papers and weakly_related_papers as input.
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Rules:
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1. Do not invent experimental results, mechanisms, references, DOI values, or figure numbers.
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2. Strong papers may define novelty, direct comparison, benchmark, and citation placement.
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3. Weak papers may support field framing, mechanism language, or writing structure, but must not be treated as direct benchmark papers.
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4. Every major claim must include author evidence, supporting DOI, or a clear status explaining that it still needs evidence.
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5. Preserve DOI strings exactly as provided.
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6. Return JSON only.
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Output JSON keys:
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- target_journal
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- article_type
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- recommended_word_template
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- material_system
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- application
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- central_claim
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- novelty_gap
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- design_principle
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- key_author_evidence
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- strong_related_papers
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- weak_related_papers
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- section_plan
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- claim_reference_map
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- must_not_overclaim
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- missing_inputs
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- qqwrite_instructions
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"""
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def main() -> int:
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parser = argparse.ArgumentParser()
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parser.add_argument("--draft", required=True, help="UTF-8 text or markdown draft path")
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@ -108,6 +148,7 @@ def main() -> int:
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profile["target_journal"] = args.target_journal
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profile["extra_notes"] = args.extra_notes
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profile["screening_prompt"] = build_prompt(profile)
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profile["brief_prompt"] = build_brief_prompt(profile)
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data = json.dumps(profile, ensure_ascii=False, indent=2)
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if args.json_out:
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