five-questions

Systematically analyze an empirical economics paper using the five-question framework. Produces a structured Chinese report (2000–3000 chars) with a transferable methodology checklist.

Input

/five-questions path/to/paper.pdf

Also accepts: /five-q path/to/paper.pdf or natural language trigger.

File locations (explicit)

This skill keeps temporary intermediate files inside the current project, not next to the source PDF, so the user's working tree stays tidy and the workspace is easy to find or clean up.

| Kind | Location | Lifecycle | |------|----------|-----------| | Skill root (this file + agents/scripts/references) | ${CLAUDE_PROJECT_DIR}/.claude/skills/five-questions/ | Versioned with the project | | Temporary workspace (paper.md + Q1–Q5 intermediate outputs) | ${CLAUDE_PROJECT_DIR}/.five-q-workspace/{timestamp}_{paper_slug}/ | Disposable; safe to delete after the final report is generated | | Final report | ${CLAUDE_PROJECT_DIR}/{paper_slug}_five_q.md | Persistent deliverable |

paper_slug = lowercase filename of the source PDF with spaces replaced by underscores and the .pdf extension stripped. timestamp = YYYYMMDD_HHMMSS.

If ${CLAUDE_PROJECT_DIR} is unset (rare — happens when the skill is invoked outside a project context), fall back to the current working directory (os.getcwd()).

Orchestration

Follow these steps exactly:

Step 1 — Validate Input

Parse the PDF path from args. If not provided, ask the user for the path.

python
import os
pdf_path = args.strip()
if not os.path.exists(pdf_path):
    print(f"ERROR: PDF not found: {pdf_path}")
    sys.exit(1)

Step 2 — Create Work Directory

Place the temporary workspace inside the current project directory (NOT next to the PDF):

python
import datetime, os, re
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
project_dir = os.environ.get("CLAUDE_PROJECT_DIR", os.getcwd())
paper_name = os.path.splitext(os.path.basename(pdf_path))[0]
paper_slug = re.sub(r"\s+", "_", paper_name).lower()
work_dir = os.path.join(project_dir, ".five-q-workspace", f"{timestamp}_{paper_slug}")
os.makedirs(work_dir, exist_ok=True)

Step 3 — Convert PDF to Markdown

Run the conversion script bundled with this skill, using the sci conda environment:

bash
conda run -n sci python3 \
    "${CLAUDE_PROJECT_DIR}/.claude/skills/five-questions/scripts/convert_pdf.py" \
    "{pdf_path}" "{work_dir}/paper.md"

If markitdown is not installed: conda run -n sci pip install 'markitdown[pdf]'

Check that {work_dir}/paper.md exists and has >2000 characters. If <2000 chars, warn the user ("可能是扫描版PDF，文本提取有限") but continue.

Step 4 — Dispatch 5 Parallel Q-Agents

Send ALL five Agent tool calls in a SINGLE message (mandatory for parallel execution). Each agent reads its role definition document first, then reads the paper and writes its answer.

Prompt template for each agent (substitute N, name, output path per agent):

你是一个实证经济学论文分析专家。你的角色定义和任务要求在以下参考文档中：

【第一步：立即读取你的角色定义文档】
${CLAUDE_PROJECT_DIR}/.claude/skills/five-questions/agents/qN-{name}.md

仔细阅读该文档，理解你的角色、任务清单和质量标准。

【第二步：读取论文正文】
{work_dir}/paper.md

【第三步：按照角色定义文档的要求完成分析，将结果保存到】
{work_dir}/QN.md

关键约束：
- 严格按照角色定义文档中的任务清单逐项完成
- 满足文档中规定的质量标准（不合格示例 vs 合格示例）
- 直接将结果写入输出文件，不要返回给主代理

Dispatch these 5 agents in parallel (one message, five Agent tool calls):

| Agent | 角色定义文档 | 输出文件 | |-------|------------|---------| | Q1 | agents/q1-research-question.md | {work_dir}/Q1.md | | Q2 | agents/q2-identification.md | {work_dir}/Q2.md | | Q3 | agents/q3-estimand.md | {work_dir}/Q3.md | | Q4 | agents/q4-robustness.md | {work_dir}/Q4.md | | Q5 | agents/q5-contribution.md | {work_dir}/Q5.md |

Step 5 — Verify Q-Files

After all 5 agents complete, check each file:

• Exists: yes/no
• Size > 500 bytes: yes/no

If any file is missing or too small, log [分析未完成] — the compiler will handle it gracefully.

Step 6 — Run Compiler Agent

Dispatch the compiler agent:

你是论文五问分析报告的汇编器。你的汇编规范在以下参考文档中：

【第一步：立即读取汇编规范文档】
${CLAUDE_PROJECT_DIR}/.claude/skills/five-questions/agents/compiler.md

仔细阅读，理解报告结构、章节格式、§6 可迁移清单的写法要求。

【第二步：读取以下所有输入文件】
- 论文全文前200行（提取元数据）：{work_dir}/paper.md
- Q1 研究问题：{work_dir}/Q1.md
- Q2 识别策略：{work_dir}/Q2.md
- Q3 核心估计量：{work_dir}/Q3.md
- Q4 稳健性检验：{work_dir}/Q4.md
- Q5 贡献与局限：{work_dir}/Q5.md

【第三步：按规范汇编完整报告，保存到】
{output_path}

关键约束：
- 总长 2000–3000 汉字
- 必须包含 § 6 可迁移方法论清单（5–8 条，具体可操作）
- 直接写入输出文件，不要返回给主代理

Output path: ${CLAUDE_PROJECT_DIR}/{paper_slug}_five_q.md (the persistent final report — see "File locations" above).

Step 7 — Report to User

五问分析完成！

最终报告（持久化）：{output_path}
临时工作目录（含 paper.md 与 Q1–Q5 中间产物，可删除）：{work_dir}/

报告结构：
  § 1 研究问题
  § 2 识别策略
  § 3 核心估计量
  § 4 稳健性检验
  § 5 贡献与局限
  § 6 可迁移方法论清单

Error Handling

| Error | Detection | Recovery | |-------|-----------|----------| | PDF not found | os.path.exists() check | Exit with clear path error | | pymupdf4llm missing | ImportError | Print: conda run -n sci pip install pymupdf4llm | | Password-protected PDF | pymupdf exception | Suggest: qpdf --decrypt input.pdf output.pdf | | Paper MD very short (<2000 chars) | char count | Warn, continue | | One or more Q-files missing | file check after agents | Compiler marks section [分析未完成] | | Metadata extraction failure | no title/author pattern | Use filename as title |

Agent Definitions

See agents/ directory:

• q1-research-question.md — 研究问题分析师
• q2-identification.md — 识别策略分析师
• q3-estimand.md — 核心估计量分析师
• q4-robustness.md — 稳健性检验分析师
• q5-contribution.md — 贡献与局限分析师
• compiler.md — 报告汇编器

Reference

Five-Question depth standards: references/five-q-framework.md