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/install weread-reading-recommender
Description
Use this skill when the user wants to export local WeRead records, normalize WeRead data, analyze reading preferences from WeRead history, or get book recomm...
Usage Guidance
This skill appears coherent and local-first. Before installing or running it: (1) review the two Python scripts yourself (they are included) and run them in a safe environment; (2) only provide your WeRead cookie locally (prefer --cookie-file or an env var), and never paste it into public chat or remote storage; (3) note that the exporter makes HTTPS requests to weread.qq.com (expected) — if you are uncomfortable providing a live cookie, you can run the normalizer against the provided sample raw JSON instead; (4) verify outputs do not contain the cookie (SKILL.md states this and the scripts are written not to include it); and (5) run the scripts with network access blocked if you only want to test normalization on sample data.
Capability Analysis
Type: OpenClaw Skill
Name: weread-reading-recommender
Version: 1.0.1
The skill is a local-first tool designed to export and normalize WeRead (微信读书) data for personalized book recommendations. The scripts (export_weread.py and normalize_weread.py) use standard libraries to interact with legitimate WeRead APIs and process data locally, with explicit safeguards in the code and SKILL.md instructions to prevent the leakage or storage of authentication cookies in logs or exported files.
Capability Assessment
Purpose & Capability
Name/description match the delivered assets. The repo contains exporter and normalizer scripts that operate on a WeRead cookie and call weread.qq.com endpoints — exactly what a WeRead export/recommendation skill would need. There are no unrelated credentials or services requested.
Instruction Scope
SKILL.md instructs the agent to check for a local cookie, run the provided export and normalize scripts, and then reason from the normalized JSON. The scripts access only local cookie sources and the official weread.qq.com endpoints; they do not read other system files or send data to unexpected endpoints. The SKILL.md also contains explicit privacy rules (do not write or echo the cookie).
Install Mechanism
No install spec is provided (instruction-only plus two local scripts). Nothing will be downloaded or installed from third-party URLs, so there is low install-time risk.
Credentials
The only sensitive input the skill uses is a WeRead cookie (via --cookie, --cookie-file, or env var like WEREAD_COOKIE). No other secrets or unrelated environment variables are requested. The cookie request is proportional to the stated functionality, but users should recognize the cookie is an authentication credential and handle it cautiously.
Persistence & Privilege
Skill is not always-enabled, does not request elevated system persistence, and there is no code that modifies other skills or global agent configs. Autonomous invocation is allowed (platform default) but does not combine with other high-risk behaviors here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install weread-reading-recommender - After installation, invoke the skill by name or use
/weread-reading-recommender - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Added planning and reference documentation: PLAN.md, SPEC.md, TODO.md
- Removed default data files: data/weread-raw.json and data/weread-normalized.json
- No functional or workflow changes; updates focus on documentation and project organization.
v1.0.0
Initial release of WeRead Reading Recommender.
- Local-first tool to export, normalize, and analyze WeRead (微信读书) reading data.
- Recommends books based on reading history and/or current learning goals.
- Checks for existing local cookie/environment sources before prompting user for setup.
- Data privacy-focused: uses local cookies only, never echoes cookies, and avoids remote storage.
- Provides structured recommendations with rationale and profile analysis, supporting both safe and exploratory picks.
Metadata
Frequently Asked Questions
What is Weread Reading Recommender?
Use this skill when the user wants to export local WeRead records, normalize WeRead data, analyze reading preferences from WeRead history, or get book recomm... It is an AI Agent Skill for Claude Code / OpenClaw, with 184 downloads so far.
How do I install Weread Reading Recommender?
Run "/install weread-reading-recommender" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Weread Reading Recommender free?
Yes, Weread Reading Recommender is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Weread Reading Recommender support?
Weread Reading Recommender is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Weread Reading Recommender?
It is built and maintained by CengSin (@cengsin); the current version is v1.0.1.
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