wjs-x-increasing-follower
Use when 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-incre
npx skills add jianshuo/claude-skills --skill wjs-x-increasing-follower --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# wjs-x-increasing-follower 把「涨粉」当工程做：**每个改动是一个带编号的实验**，有假设、有目标指标、有 before（可回滚）、有判决。不靠感觉，靠数。 ## Core Principle **单账号没法做平行 A/B —— 只能做时间轴上的前后对比。** 所以唯一可信的北极星指标是 **转化率 = 新增关注 ÷ 主页访问（ratio）**：它对爆款流量免疫。一条推爆了带来一堆访问，ratio 不一定动；但 bio 改好了，每个来访的人更愿意关注，ratio 一定动。 **所以指标分层（每个 action 必须声明自己被哪个指标考核）：** | Action 类型 | 拨动的杠杆 | 用什么考核 | |---|---|---| | **profile**（bio / 名字 / 头像 / banner / 置顶 / URL / 地点） | 转化 | **ratio**（北极星，抗爆款） | | **posting**（格式 / 钩子 / 频率 / thread vs 单条） | 触达 | profile visits + impressions（ratio 当护栏，别把转化拖垮） | | **engagement**（回复 / 关注别人 / 互动） | 触达 | new follows + visits | | **timing**（发布时间） | 触达 | profile visits | **诚实护栏（写死在 evaluate.py 里）：** 用中位数不用均值（一天爆款骗不了判决）；够 7 天 / 够数据才下判决；同指标实验窗口重叠 → 打 `confounded` 标记；只给「方向性」结论，不号称因果。 **回滚是一等公民。** 每个 action 存了精确的 before 值，#N 永远能还原。判 ❌ rollback **先问王建硕，绝不静默改他的 bio**。 ## 数据从哪来（关键约束） ratio 这个数 **X API / xurl 拿不到** —— 只活在 Analytics 看板里。所以靠 **CSV 导出**： 打开 `x.com/i/account_analytics` → Overview → 右上角下载图标（7D/2W/4W/3M/1Y 想要哪段先选好）→ 导出 CSV → 丢进 `inbox/`（或直接给路径）。 ## When This Skill Fires - 王建硕说「涨粉」「搞个 X 涨粉实验」「A/B 测一下我的 profile」「今天的涨粉检查」 - 跑 `/wjs-x-increasing-follower` - 设了 `/schedule daily /wjs-x-increasing-follower`（见末尾「每日检查」） ## When NOT to use - 只是要发
- Core Principle
- 数据从哪来（关键约束）
- When This Skill Fires
- When NOT to use
- Workflow
- Step 1 — 吃数据（CSV → daily.jsonl）
- Step 2 — 出 to-do（提实验）
- Step 3 — 上线一个实验（含抓 before）
- Step 4 — 每天喂数 + 重算（见也用于「每日检查」）
- Step 5 — 判决与回滚
- Step 6 — 看板
- 数据模型（state/）
- 默认参数（要改就传 flag）
- 每日检查（可选调度）
python3 scripts/ingest-csv.py /path/to/export.csv python3 scripts/ledger.py add --category profile --metric ratio \ xurl "/2/users/me?user.fields=name,description,url,location,profile_image_url,pinned_tweet_id" xurl --auth oauth1 -X POST "/1.1/account/update_profile.json?description=<urlencoded>" python3 scripts/ledger.py apply 1 \ scripts/daily-check.sh python3 scripts/evaluate.py # 看每个在跑实验：baseline vs post，Δ%，verdict python3 scripts/scoreboard.py # 写并打印 state/SCOREBOARD.md
What does the wjs-x-increasing-follower skill do?
Use when 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-incre
How do I install it?
Run `npx skills add jianshuo/claude-skills --skill wjs-x-increasing-follower --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jianshuo/claude-skills, a repository with 112 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.