
Huashu WeRead
https://github.com/alchaincyf/huashu-weread- Category
- Developer Tools
- Rank
- No. 1397Tools index
Previous survey · No. 1402 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- alchaincyf
- GitHub
- 145 stars
- Date
About
WeChat Reading advanced advisor — a Claude Code skill that adds a reading-advisor workflow on top of the official weread skill.
What it does
Huashu WeRead turns personal reading history into four guided outputs: next-book recommendations, staged learning paths, structured note summaries, and period reviews. It compares bookshelf entries with notebook activity, infers reading depth from note counts, checks recent activity, and verifies that recommended books are available before linking them.
Why it's ranked here
The strongest idea is its refusal to treat a saved book as a read book. Cross-checking shelves, notes, and recent activity gives recommendations a defensible personal basis. Explicit checkpoints, availability checks, fallback behavior, and a single strongest recommendation make the workflow unusually actionable. Its quality still depends on complete account data and heuristic thresholds.
What's good
Each workflow has a clear decision process rather than a loose prompt. Recommendations identify missing schools, time periods, abstraction levels, or neighboring subjects. Learning paths add staged reading, time estimates, self-check questions, and an acceptable stopping point. Note synthesis clusters ideas instead of dumping highlights. Error handling tells users when data is missing, retries API failures once, and avoids silent degradation.
Tradeoffs
It requires the official WeRead skill, an API key, and live access to the WeRead gateway. Note-count thresholds can underrate readers who rarely annotate, while title-keyword matching may classify topics crudely. New or empty accounts lose much of the personalization. Several checkpoints require user input, and review publishing or social-image creation depends on separate skills rather than this package.
How to use it well
Use it when your WeRead account contains enough shelf and note history to reveal patterns. It fits readers choosing a next book, entering a subject systematically, consolidating highlights, or drafting a periodic reading retrospective. State the topic, desired recommendation count, availability preference, or publishing format upfront. Do not use it as a replacement for the official data connector or as a direct publishing system.
Technical notes+
SKILL.md defines routing, gateway requirements, shared error handling, display rules, and the WEREAD_API_KEY dependency. workflows/advisor.md, workflows/path.md, workflows/alchemy.md, and workflows/review.md contain separate prompt-driven procedures. shared/knowledge-map.md defines the shelf and notebook heuristics, while shared/shelf-cross-notes.md includes a Python template that calls POST https://i.weread.qq.com/api/agent/gateway through curl and reads the official skill version from ~/.claude/skills/weread/SKILL.md. test-prompts.json contains three routing examples, not an executable test harness. examples/advisor-neuroscience.md documents one end-to-end recommendation case.
Observed
- License
- MIT, permitting personal and commercial use.
- Packaging
- Installed through the skills CLI with npx skills add alchaincyf/huashu-weread.
- Interface
- Prompt-driven agent skill layered over the official WeRead HTTP API gateway.
- Dependency
- Requires the official WeRead skill and a WEREAD_API_KEY environment variable.
- Agent support
- README lists Claude Code, Cursor, Codex, OpenClaw, and Hermes.
- Repository structure
- Four workflow documents, shared methodology documents, one worked example, and a JSON prompt fixture.
Read from README.md, LICENSE, SKILL.md, test-prompts.json, workflows/path.md, workflows/review.md, workflows/advisor.md, workflows/alchemy.md, shared/knowledge-map.md, shared/shelf-cross-notes.md, examples/advisor-neuroscience.md.
What it can do
Provide reading recommendations
User reading preferences and history → Personalized book recommendations
Analyze reading patterns
WeChat Reading user data → Reading behavior insights and statistics
Generate reading advice
Book content and user reading goals → Customized reading guidance and tips
Track reading progress
User reading activity from WeRead → Progress reports and milestone updates
Create reading workflows
User reading objectives and preferences → Structured reading plans and schedules
Tags
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.