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Weread Reading Recommender

当用户想要导出本地微信读书记录、规范化微信读书数据、分析阅读历史偏好或获取图书推荐时使用此技能

person作者: cengsinhubclawhub
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WeRead Reading Recommender

Overview

This is a local-first skill for exporting 微信读书 (WeRead) records from a cookie stored on the user's machine, normalizing those records into a recommendation-friendly JSON file, and using that data to analyze reading preferences or recommend what to read next.

Use this skill when the user wants to:

  • 根据微信读书记录推荐书
  • 分析自己的阅读偏好或阅读画像
  • 结合“最近想学的主题”与微信读书历史一起做推荐
  • 导出、刷新、归一化本地微信读书数据

Trigger Cases

Activate this skill for requests like:

  • “根据我的微信读书记录推荐书”
  • “分析我的阅读偏好”
  • “我最近想系统学 AI Agent，结合微信读书记录推荐 5 本书”
  • “帮我导出 / 刷新 / 归一化微信读书数据”
  • “基于我的阅读历史，推荐下一本最适合现在读的书”
  • “分析我的阅读偏好，并给我 3 本稳妥推荐 + 2 本探索推荐”

Workflow

Follow this sequence:

  1. Check whether a normalized JSON file already exists.
  2. If normalized data is missing, or the user explicitly wants fresh data, check whether a local WeRead cookie is already available.
  3. Look for a local cookie source in this order:
    • a cookie file path explicitly provided by the user
    • WEREAD_COOKIE
    • another env var name passed through --env-var
  4. If no local cookie source exists, ask the user to set one locally and stop there. Do not tell the user to edit SKILL.md.
  5. If a local cookie source exists, run the export script.
  6. Run the normalize script on the raw export.
  7. Read the normalized JSON and identify strong signals:
    • high-engagement books
    • recent books
    • unfinished books with momentum
    • repeated categories or lists
  8. If the user provides a current goal, weight goal fit first.
  9. If the user does not provide a goal, produce a reading-profile summary plus safe and exploratory recommendations.

Recommendation Guidance

When the user provides a current goal, weight approximately:

  • 60% goal fit
  • 40% history fit

When the user provides no goal, weight approximately:

  • 70% history fit
  • 20% recency
  • 10% exploration/diversity

For each recommendation, explain:

  • why it fits the user's current goal or history
  • which past books it resembles
  • what gap it fills
  • whether it is a safe pick or an exploration pick
  • whether it is a good fit right now

Suggested response structure:

  • 阅读画像 / Reading profile
  • 推荐结果 / Recommendations
  • 为什么适合现在 / Why now
  • 暂缓推荐 / Skip for now (optional)

Local Data Workflow

1. Check local cookie availability first

Before asking the user to set anything, first check whether a local cookie is already available through:

  • a cookie file path the user provided
  • WEREAD_COOKIE
  • another env var name passed through --env-var

If none of these exist, ask the user to set the cookie locally, then continue.

2. Export raw WeRead data

If a local cookie is already available, export directly:

python3 scripts/export_weread.py --out data/weread-raw.json

Optional variants:

python3 scripts/export_weread.py --cookie-file ~/.config/weread.cookie --out data/weread-raw.json
python3 scripts/export_weread.py --env-var WEREAD_COOKIE --include-book-info --detail-limit 50 --out data/weread-raw.json

If the user does need to set one manually, keep it local. For example:

export WEREAD_COOKIE='wr_skey=...; wr_vid=...; ...'

3. Normalize the raw export

python3 scripts/normalize_weread.py --input data/weread-raw.json --output data/weread-normalized.json

4. Use the normalized file for recommendation turns

After normalization, this skill should reason primarily from the normalized JSON, not from a live cookie session, unless the user explicitly asks for a refresh.

Security Boundary

This skill is local-first. Enforce these rules:

  • Cookie is for local use only.
  • Never write the cookie into SKILL.md, scripts, assets, logs, or exported JSON.
  • Never echo the cookie back in responses.
  • Prefer checking existing local cookie sources before asking the user to set one again.
  • Do not rely on CookieCloud or any third-party cookie sync service by default.
  • Do not suggest remote cookie hosting as the normal path.
  • Recommendation work should use the normalized JSON whenever possible.

Files

Use these project files as the main references:

  • scripts/export_weread.py
  • scripts/normalize_weread.py
  • references/data-schema.md
  • references/privacy-model.md
  • references/recommendation-rubric.md
  • assets/sample-weread-raw.json
  • assets/sample-weread-normalized.json

Example Requests

  • 结合我的微信读书记录，我最近想系统学 AI Agent，推荐 5 本书
  • 基于我的阅读历史，推荐下一本最适合现在读的书
  • 分析我的阅读偏好，并给我 3 本稳妥推荐 + 2 本探索推荐
  • 帮我刷新微信读书数据，然后按最近在读主题推荐下一批书