paper-writing-assistant

name	paper-writing-assistant
description	Central hub for academic paper writing with selectable author styles. Use when the user asks to write, revise, evaluate, or distill any section of an academic paper.

Paper Writing Assistant

Use this skill as the central controller whenever the user wants to draft, revise, evaluate, or distill an academic paper section in a specific author's writing style.

Capabilities

• Write: Draft a specific section using a selected author's style.
• Revise: Rewrite an existing section to match a selected author's style.
• Evaluate: Score a section against the author's style rubric.
• Distill: Run the factory pipeline to create a new author style from PDFs.

Project Base Path

The PaperSkill project is located at:

• WSL: /mnt/g/project/PaperSkill/
• Windows: G:\project\PaperSkill\

All operations must load registry.json from the project base first.

Workflow

Step 1: Determine Intent

Parse the user's request into one of:

• write
• revise
• evaluate
• distill

If unclear, ask: "你是想写/改/评某个 section，还是蒸馏新文风？"

Step 2: Determine Author

1. Load registry.json from the project base.
2. Check if the user explicitly mentions an author name or ID.
   • Known aliases: "唐旭" → xu-tang, "Xu Tang" → xu-tang.
3. If no author is specified, use registry.json["default_author"].
4. Validate that the author exists in registry.json["authors"].
   • If not found, list available authors and ask the user to choose.

Step 3: Determine Section

Attempt to infer the section from the user's message. Supported mappings:

Section	Keywords
abstract	abstract, 摘要
introduction	introduction, 引言, 简介
related_work	related work, 相关工作, 文献综述
methods	methods, 方法, 方法论
results	results, 结果, 实验结果
discussion	discussion, 讨论
conclusion	conclusion, 结论, 总结

If multiple sections are mentioned, ask which one to proceed with. If no section is detected and intent is write/revise/evaluate, ask: "请告诉我你要处理的 section（如 introduction / methods / results 等）。"

Step 4: Load Assets

For the resolved (author, section) pair, load:

• Skill rules: authors/<author_id>/skills/skill_<section>.json
• Prompt template: authors/<author_id>/prompts/prompt_<section>.json
• Rubric (for evaluate): authors/<author_id>/rubric.json

If files are missing, fall back to the author-style sub-skill references:

• hermes-skills/author-styles/<author_id>-paper-style/references/section-skills.md
• hermes-skills/author-styles/<author_id>-paper-style/references/prompts-and-rubric.json

Step 5a: Write / Revise

1. Summarize the user's provided facts, figures, tables, and claims.
2. If facts are insufficient, mark missing items instead of inventing them.
3. Build a short outline following the loaded skill's structure and logic_pattern.
4. Output the outline first, then the draft.
5. For revise tasks, highlight what was changed to match the author's style.

Step 5b: Evaluate

1. Load the rubric JSON.
2. Score the provided text across the 6 dimensions (1-5 scale).
3. Compute the average score.
4. Provide specific improvement suggestions for any dimension scoring ≤3.

Step 5c: Distill

When the user asks to distill a new author style, use the two-phase workflow below. Hermes acts as the LLM for the enrichment phase; no external LLM API configuration is required.

Phase 1: Skeleton Generation (terminal)

1. Ask the user for the new author's ID, display name, and domain.
2. Verify that authors/<new_id>/papers/ contains PDFs or extracted text files.
3. If the aggregation report does not exist yet, instruct the user to run their existing analysis scripts first (e.g., place extracted texts in authors/<new_id>/extracted_texts/ and run the analyzer).
4. Run the skeleton pipeline:

   python factory/run_pipeline.py --author <new_id> --no-llm
   

   This generates stable skill skeletons, prompts, rubric, and Hermes references without LLM enrichment.

Phase 2: Hermes Enrichment (Hermes direct)

5. For each of the 7 sections, load:
   • authors/<new_id>/analysis/aggregation_report.json
   • authors/<new_id>/skills/skill_<section>.json
6. Read the relevant parts of the aggregation report (surface style, syntactic style, rhetorical structure for the section, academic stance).
7. Use your own reasoning to generate:
   • preferred_phrases: 5-8 complete sentence templates or starter phrases reflecting the author's style
   • logic_pattern: 1-3 strings describing the rhetorical flow
8. Write the enriched fields back into each skill_<section>.json.

Phase 3: Rebuild (terminal)

9. Run:

   python factory/rebuild_after_enrich.py --author <new_id>
   

   This rebuilds prompts, rubric, and Hermes references from the enriched skills.

Phase 4: Registry Update

10. Append the author metadata to registry.json["authors"].
11. If the user says "设为默认" or "set as default", update registry.json["default_author"].

Example request: "我想蒸馏一个新导师的风格，论文已经放在 authors/prof-li/papers/ 里了。"

• Response: "好的。请告诉我这位导师的显示名称和研究领域（例如 prof-li / 计算机视觉）。接下来我会先跑骨架脚本，然后由我直接为每个 section 填充文风特征，最后同步生成 prompts 和 references。"

Output Discipline for Drafting

1. Outline first: Always present the section outline before the body text.
2. No hallucination: Do not invent citations, datasets, numbers, or code links.
3. Section fidelity: Do not write content belonging to other sections.
4. Style proximity: Match rhetoric, progression, and claim strength; do not copy source wording verbatim.
5. Missing info: If critical inputs are absent, explicitly list them as [待补充: ...] instead of filling them in.

Registry Update Rule

Whenever a new author style is successfully distilled:

• Append the author metadata to registry.json["authors"].
• If the user says "设为默认" or "set as default", update registry.json["default_author"].

Example Interactions

User: "用唐旭老师的风格帮我写 Introduction，关于遥感变化检测的。" → Intent: write, Author: xu-tang, Section: introduction. Load skill, ask for facts if missing, then output outline + draft.

User: "把这段 results 改成唐旭风格。" → Intent: revise, Author: xu-tang, Section: results. Load skill, rewrite, highlight changes.

User: "评估一下这段 abstract 符不符合唐旭的风格。" → Intent: evaluate, Author: xu-tang, Section: abstract. Load rubric, score, give feedback.

User: "我想蒸馏一个新导师的风格，论文已经放在 authors/prof-li/papers/ 里了。" → Intent: distill, Author ID: prof-li. Run pipeline, update registry.