Stock Qualitative Analysis Skill

When to use

Use this skill when a user asks for a qualitative stock analysis report (定性分析) that must be evidence-based and formatted as a structured report. This skill emphasizes strict citations and non-hallucination behavior.

Inputs

• Company name (required)
• Ticker / exchange (optional but recommended)
• Market context (US / HK / CN / other)
• Time window (e.g., FY2015–FY2024; latest quarterly)
• Language preference (Chinese default; English if requested)
• Sources:
  • User-provided filings (PDF or HTML)
  • SEC EDGAR fetch (optional, if allowed)
  • Other public sources (only if cited)

Outputs

• A Markdown report following the template structure in assets/report-template.md
• Each section contains: 结论要点 / 详细情况 / 证据与出处 (or English equivalents when English output is requested)
• Final 来源清单 with SEC filings and other sources in reverse-chronological order

Core rules (non-negotiable)

• Do not state facts without a source.
• Any factual claim MUST include a source string; otherwise use a placeholder in 【...】 describing what is needed.
• Actively analyze sources: Go beyond surface-level summaries. Extract specific details, quantitative data, and contextual insights relevant to each section of the report template.
• Comprehensive filling: Make the best effort to fill all sections of the report template. If information is truly missing from the provided sources, use a specific placeholder indicating what is missing.
• If real-time data is required, explicitly state that the user must verify freshness.
• No investment advice, price targets, or trading recommendations.
• Default output language: Chinese. If the user query is in English, respond in English.

Execution

• Intake: confirm company name, ticker/exchange, market, time window, and allowed data sources.
• Pre-check local data: before any remote fetching, verify whether local filings are sufficient; only fetch remotely if local data is insufficient.
• Acquire sources: use scripts/build_source_manifest.py to pull SEC filings and ingest local PDFs.
• Extract key 10-K sections (HTML): use scripts/extract_sec_html_sections.py to produce per-item text files (e.g., Item 1/1A/7/8) before analysis.
• Section-by-section generation (Agent-driven): for each section in assets/report-template.md, the Agent expands the section in sequence, producing 结论要点 / 详细情况 / 证据与出处 based on the available sources and citing evidence.
• Progressive write-back: before starting summaries, determine whether a local report file exists; after completing each section, write the content into that file.
• Finalization: rewrite 投资要点概览 after all sections are complete, then update 来源清单.

Usage

• The Agent executes the section loop at runtime based on the template headings.
• The Agent MUST attempt to fill every section using provided sources and mark missing facts with explicit placeholders.
• If the user asks for English output, the Agent translates the template headings and section labels consistently (e.g., Conclusion / Details / Evidence) while preserving the report structure.

Data acquisition

• SEC EDGAR fetch: scripts/fetch_sec_edgar.py
• Local PDF ingestion: scripts/ingest_local_pdfs.py
• Source manifest: scripts/build_source_manifest.py
• HTML section extractor: scripts/extract_sec_html_sections.py

Citation format

• SEC filings: Form 10-K/10-Q/20-F/6-K + 年度/日期 + 章节/标题
• Web sources: 机构/网站 + 发布日期 + 标题

Examples

Example request

“参考 SEC filings，帮我做 AAPL 的定性分析，按模板输出。”

Example output shape

Use assets/report-template.md and fill each section with facts + citations. Unknowns become placeholders.

References

• Guardrails and writing style: references/prompt-guardrails.md
• Report template: assets/report-template.md
• Validation checklist: references/validation-checklist.md
• Goldenset examples: references/goldenset.md