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baokuan-article-analysis

Fetch and analyze WeChat Official Account hot articles / 公众号爆款文章 by sector or keywords. Use when the user asks for 爆款文章分析, 赛道爆款, 公众号爆款数据, reading counts, likes, shares, comments, title patterns, writing style, 爆款原因分析, or writing references for content creation.

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See how to install

Files included (4)

  • SKILL.md3.4 KB
  • agents/openai.yaml251 B
  • references/default-sectors.json377 B
  • scripts/daily_sector_trends.py36.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

爆款文章分析

Overview

Use this skill to fetch hot WeChat Official Account article data by sector and generate a daily analysis report. It is for sector-level or keyword-level analysis, not exact historical scraping for one specific account.

The bundled script queries the hot-article data source, merges keywords by sector, deduplicates articles, ranks them, and writes:

  • data.json: raw structured data
  • report.html: a minimal visual analysis report with KPI cards, bar charts, ranked article cards, writing style analysis, hot reasons, and writing references

Do not default to Markdown reports. The primary user-facing artifact is report.html.

Quick Start

Run with the default sectors:

python3 ~/.codex/skills/baokuan-article-analysis/scripts/daily_sector_trends.py \
  --output-dir ./output/baokuan-article-analysis

Run custom sectors:

python3 ~/.codex/skills/baokuan-article-analysis/scripts/daily_sector_trends.py \
  --sector 'AI Agent=AI Agent,智能体,Agent框架' \
  --sector 'Skill=skill,Skills,AI Skill' \
  --sector 'Claude Code=Claude Code,Codex,AI编程' \
  --output-dir ./output/baokuan-article-analysis

Run with a JSON config:

python3 ~/.codex/skills/baokuan-article-analysis/scripts/daily_sector_trends.py \
  --sector-config ~/.codex/skills/baokuan-article-analysis/references/default-sectors.json \
  --days 7 \
  --output-dir ./output/baokuan-article-analysis

Workflow

  1. Identify sectors and keywords from the user request.
  2. If the user only gives broad sectors, use or adapt references/default-sectors.json.
  3. Run scripts/daily_sector_trends.py.
  4. Open the generated report.html.
  5. Summarize for the user:
    • highest-reading and highest-sharing articles
    • writing style patterns
    • hot article reasons
    • title and topic formulas
    • practical writing references
  6. Return the HTML file path as the main artifact.

Script Options

OptionPurpose
--sector '赛道=关键词1,关键词2'Add one sector. Can repeat.
--sector-config path.jsonLoad sectors from JSON object.
--days NLookback window. Default is 7 days.
--start-date YYYY-MM-DDExplicit start date. Overrides --days.
--max-items-per-sector NLimit ranked articles per sector. Default is 10.
--output-dir DIRParent output directory. A date folder is created inside.
--report-date YYYY-MM-DDReport date. Default is today.

Data Boundaries

  • This skill returns hot-list data, not a specific account’s complete recent history.
  • clicksCount is a public data-source snapshot and may lag behind live WeChat backend reads.
  • If a specific account name returns no data, switch to that account’s topic keywords and compare same-sector articles.
  • If today’s data is sparse, use --days 7 or --days 30.

Output Interpretation

Use reading count for reach, share count for spread, comments for discussion, and low-fan high-reading entries for title/structure references. Repeated accounts indicate strong competitors or content sources worth following.

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