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GKTJ Pt Skill

Use when generating patient-facing questionnaire analysis reports from uploaded survey spreadsheets or questionnaire tables, especially when the output must...

personAuthor: lee-luogenhubclawhub
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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