wewrite-stats

Original：🇨🇳 Chinese

Translated

WeWrite Data Review Module: Pulls reading/sharing/like data from WeChat Official Account articles, backfills historical records, and provides adjustment suggestions for topic selection, titles, and frameworks. Trigger keywords: check article data, how's the article data, effect review, check performance, how's the view count. Requires Official Account/WeWrite context; should not be triggered by generic "data analysis".

9installs

Sourceimraywang/wewrite

Added on2026-10-09

NPX Install

npx skill4agent add imraywang/wewrite wewrite-stats

SKILL.md Content (Chinese)

View Translation Comparison →

wewrite-stats — Article Data Review

Operating Conventions

CLI: Deterministic operations use the
```
wewrite
```
command (must be in PATH; if missing, guide users to run
```
uv tool install wewrite
```
, or run
```
bash install.sh
```
in the repository).
{home}: User state directory =
```
$WEWRITE_HOME
```
or
```
~/.wewrite
```
(can be checked with
```
wewrite home
```
). config/style/history/playbook/output/exemplars are all located in {home}, not in the repository; state paths in references documents follow the same convention.
读取: <路径>
= Actually read the entire file using a file reading tool before proceeding, this is not a comment.
references/: This skill comes with
```
{skill_dir}/references/
```
;
```
{skill_dir}
```
in references documents refers to the directory of this skill.

Execution

读取: {skill_dir}/references/effect-review.md

Execute according to its process: run

fetch_stats.py --days 7

to pull data → match and backfill the stats field of

history.yaml

→ analyze the best/worst performing content and their reasons → provide adjustment suggestions for future topic selection, titles, and frameworks.

Prerequisites: WeChat API credentials in config.yaml are required. If credentials are missing → inform the user that "data review requires configuring the Official Account API (config.yaml), currently only qualitative analysis can be performed based on existing records in history.yaml", then analyze as much as possible based on the existing content of history.yaml. For newly published articles → inform the user to check again after 24 hours.

Downstream Impact: The backfilled stats will be read by the topic selection module (wewrite-topic) — frameworks/enhancement strategies with good performance will be weighted higher in the next recommendation. This is half of the data closed loop; the other half is the content revision flywheel of wewrite-learn.