GKTJ Pt Skill

Use when generating patient-facing questionnaire analysis reports from uploaded survey spreadsheets or questionnaire tables, especially when the output must include fixed sections, consistent charts, controlled Word typography, and restrained patient-facing wording.

GKTJ Patient Survey Report Generator

Overview

This skill generates patient-facing questionnaire analysis reports with a fixed structure, controlled wording, and .docx output. The repository is currently migrating from a lightweight direct renderer toward a template-driven patient report workflow with fixed .docx templates and Word native editable charts.

When to Use

• Uploaded attachment contains patient questionnaire data, especially .xlsx, .csv, or copied survey tables.
• Output must follow a fixed patient report structure rooted in: 引言 / 数据信息分析 / 反馈意见分析 / 附件.
• Every question in chapter 2 needs a matching chart.
• The report must stay in patient viewpoint throughout.
• The user wants .md and .docx artifacts, not just chat output.

Do not use this skill for doctor reports, clinical trial manuscripts, or unstructured brainstorming.

Workflow

1. Normalize inputs:
   • Required: 品种, 地区, questionnaire attachment.
   • Optional: 时间, custom execution note, custom output directory.
2. Parse the questionnaire data first.
   • Use scripts/parse_questionnaire.py for spreadsheet inputs.
   • Save structured output as questionnaire.json before writing report sections.
3. Derive one statement-style title per question.
   • These titles are the chapter 2 dimension headings.
4. Generate report content in this order:
   • 引言
   • 2、数据信息分析 question by question
   • 3.1 积极反馈
   • 3.2 待改进反馈
   • 4、附件-问卷题目内容
   • When drafting prose, use the patient expression modules in references/expression-modules.md.
5. Build a report payload JSON through a script, not by hand.
   • AI should first write report_content.md or report_content.jsonl.
   • Use scripts/build_payload.py to convert that draft plus questionnaire.json into a valid report_payload.json.
6. Render artifacts.
   • Legacy path: use scripts/render_report.py to generate charts, markdown, docx, and a summary JSON.
   • Target path: fill a fixed .docx template and update template-native Word charts.
   • The template-driven rules live in references/template-spec.md.
7. Verify before delivery.
   • Chart count must equal chapter 2 question count.
   • Attachment must preserve original question and option meaning.
   • No absolute efficacy or safety claims.

Required Output Rules

• Patient viewpoint only. Do not rewrite as doctor judgement.
• Do not invent sample size, hospitals, institutions, or dates.
• If sample size or time is missing, use restrained wording and omit unsupported facts.
• In chapter 2, analyze percentage relationships; do not mechanically enumerate A/B/C/D and stop there.
• In chapter 2, each question should preferentially use one of these 3 analysis angles:
  • analyze option percentages and give a conclusion plus feasible suggestion
  • analyze the percentage of major options one by one and explain possible causes
  • analyze the overall percentage structure and summarize the group-level pattern
• The model may randomly choose any one of the 3 angles question by question, but the chosen angle must still fit the actual data distribution.
• In chapter 2, always decide the pattern first, then write:
  • overall recognition
  • conditional recognition
  • behavioural split
  • pain-point attention
• Translate percentage structure into: patient experience, self-management behaviour, convenience, understanding, support needs.
• Do not turn patient questionnaire feedback into efficacy proof or clinical recommendation.

Section Rules

• Read references/section-rules.md before generating text.
• Read references/compliance-rules.md before finalizing text.
• Read references/execution-rules.md before building the report payload.
• Read references/expression-modules.md before drafting narrative paragraphs.

Scripts

• scripts/parse_questionnaire.py
  • Reads questionnaire spreadsheets and emits normalized JSON.
• scripts/build_payload.py
  • Reads questionnaire.json plus structured report content and emits a validated report_payload.json.
• scripts/render_report.py
  • Legacy renderer for the original lightweight flow.
  • Generates markdown, PNG charts, .docx, and a summary JSON.
  • This path is not the long-term solution for customer-template parity.
• scripts/render_from_template.py
  • Loads the fixed patient .docx template and writes structured payload v2 text back into the template.
• scripts/update_word_charts.py
  • Updates pre-seeded Word native editable charts inside the rendered .docx.

Expected File Flow

• Input:
  • attachment spreadsheet
• Intermediate:
  • questionnaire.json
  • report_content.md or report_content.jsonl
  • report_payload.json or report_payload.v2.json
• Output:
  • report_draft.md
  • report_final.md
  • charts/chart_XX.png for legacy flow only
  • report_rendered.docx for template-driven flow
  • 问卷调研分析报告-{{品种}}-患者端-{{地区}}.docx
  • report_summary.json

Common Mistakes

• Writing in doctor viewpoint.
• Letting chapter 3 repeat chapter 2 item by item.
• Asking the model to handwrite a long report_payload.json with many Chinese paragraphs.
• Turning patient feedback into “证明疗效” or “安全性确证”.
• Forgetting that every chapter 2 item needs one chart and only one chart.
• Extending the legacy direct renderer when the request is really about the template-driven workflow.

Final Delivery

Reply with:

• markdown final path
• docx path
• chapter completeness check
• chart count check
• chart style consistency check
• missing or uncertain data
• unresolved issues