Agent skill

Wjs X Improving Content

by jianshuo in jianshuo/claude-skills

A skill your agent uses when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and…

MITAuto-check passedWriting & Content

Install Wjs X Improving Content

skills CLI
$ npx skills add jianshuo/claude-skills --skill wjs-x-improving-content -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jianshuo/claude-skills wjs-x-improving-content --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jianshuo/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/wjs-x-improving-content .claude/skills/wjs-x-improving-content && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
wjs-x-improving-content
GitHub stars
131
Token cost
~1.1k tokens
SKILL.md length
260 words
Files
12 (incl. scripts)
Skills in repo
38
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and…

  • Works in 5 steps: 吃数据 → 挖内容特征(核心,立刻有用) → 提一版 prompt 改动(带假设) → …
  • 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md
  • SKILL.md covers Core Principle, 版本 = prompt.md 的 git short-SHA, 数据从哪来 and When This Skill Fires, plus 5 more sections
  • Runs Python scripts from its folder; calls python3 and git

What it does

Wjs X Improving Content is an agent skill from jianshuo/claude-skills. Use when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed…

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts (for example `scripts/_common.py`, `scripts/analyze-content.py` and `scripts/evaluate.py`).

It sits in Writing & Content, covering Social media posts. It works with X (Twitter) and Git. The repository describes itself as: 13 Claude Code skills for video production (transcribe / translate / dub / multicam / subtitles / reframe) + WeChat publishing. Compatible with Claude Code, OpenAI Codex CLI… The licence is MIT.

When your agent uses it

  • 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md
  • Used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets
  • 什么内容 impression 高
  • Improve my tweets

Example prompts

  • “改 X 的 prompt”
  • “X 内容改进”
  • “哪版 prompt 最好”
  • “/wjs-x-improving-content”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. 吃数据
  2. 挖内容特征(核心,立刻有用)
  3. 提一版 prompt 改动(带假设)
  4. 攒够样本后判决
  5. 看板

What it can do on your machine

Read from SKILL.md and the folder at commit b2690f5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 6 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Wjs X Improving Content loads about 1.1k tokens when it runs. Until then it costs about 187 tokens; SKILL.md has 260 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~187
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jianshuo/claude-skills at commit b2690f5, republished under its MIT licence (© jianshuo). 260 words, ~1,142 tokens.

Download SKILL.mdSave it as .claude/skills/wjs-x-improving-content/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
wjs-x-improving-content
description
Use when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed the next prompt edit. North-star = impressions per tweet. Triggers — "改 X 的 prompt", "X 内容改进", "哪版 prompt 最好", "什么内容 impression 高", "improve my tweets", "A/B test the X prompt", "/wjs-x-improving-content".

wjs-x-improving-content

把「写好推」当工程做:不断改 prompts/x/prompt.md,用 impression 数据看哪版最好,并挖出「什么内容特征和高 impression 相关」反哺下一版。是 [[wjs-x-increasing-follower]] 的孪生——那个测 profile→关注转化率,这个测 prompt→每条推的 impression。

Core Principle

impression 主要由源文章 / 话题决定,prompt 只是二阶因素。 一篇好文章配任何 prompt 都能爆。所以诚实地分两层看:

看什么信号强度怎么用
prompt 版本对比(哪版 prompt 的推中位 impression 高)弱(被文章支配,需大量样本)方向性参考,攒够样本才下判决
内容特征(angle A/B/C、长度、钩子——prompt 直接控制的东西)较强(同样话题下,特征差异才显出 prompt 的手艺)真正反哺 prompt 的依据

所以:版本对比给方向,内容特征给抓手。 别把版本判决当因果。

判决用中位数不用均值(impression 极度长尾,一条爆款骗死均值);每版至少 5 条成熟推才下版本级判决;成熟窗 = 发布满 3 天(impression 还在涨的太新推不计入)。

回滚是一等公民:prompt 在 git 里,回滚 = git checkout <旧SHA> -- prompts/x/prompt.md。

版本 = prompt.md 的 git short-SHA

每条推归到哪版 prompt,按时间推导:推发布时间 T → prompts/x/prompt.md git 历史里时间 ≤ T 的最后一次提交 = 那条推用的版本。不用改 Action,历史推也能回填。早于 prompt 文件存在的推 → prompt_sha=null(pre-prompt)。

数据从哪来

每条推的 impression X API 不稳,靠 Content CSV 导出:x.com/i/account_analytics → Content 标签 → 导出 CSV(含 Post id / Impressions / Engagements …)→ 丢进 inbox/。Post id 就是 tweet_id,和发推历史对得上。

When This Skill Fires

  • 「改 X 的 prompt」「哪版 prompt 最好」「什么内容 impression 高」「X 内容改进」
  • 跑 /wjs-x-improving-content

When NOT to use

  • 涨粉 / 改 profile → [[wjs-x-increasing-follower]]
  • 只是发一条推 → /wjs-tweeting-from-articles
  • 推广 skill → /wjs-promoting-skills

Workflow

脚本在 scripts/,状态在 state/。先 cd 到 skill 目录。

Step 1 — 吃数据
bash
python3 scripts/ingest-tweets.py /path/to/content.csv

join Content CSV + 发推历史(~/.claude/skills/wjs-tweeting-from-articles/state/history.jsonl,带 slug/angle)→ state/tweets.jsonl,按日期推导 prompt_sha,算 char_len 和 mature(≥3天)。upsert,重跑更长导出安全。

Step 2 — 挖内容特征(核心,立刻有用)
bash
python3 scripts/analyze-content.py          # 成熟推
python3 scripts/analyze-content.py --all     # 含未成熟(angle 样本更全)

按 angle / 长度 / 来源拆 impression 中位数 + 互动率,列最高/最低推。这层告诉你 prompt 该往哪改。

Step 3 — 提一版 prompt 改动(带假设)

据 Step 2 的信号,对 prompts/x/prompt.md 做一个可证伪的改动(例:「偏短句」「在拿不准时优先选金句 angle」)。改完 commit:

bash
cd ~/code/wechat-publish
# 编辑 prompts/x/prompt.md ...
git add prompts/x/prompt.md && git commit -m "x prompt: <一句话改了啥>"
NEW_SHA=$(git log -1 --format=%h -- prompts/x/prompt.md)

登记成编号实验:

bash
python3 ~/.claude/skills/wjs-x-improving-content/scripts/ledger.py register "$NEW_SHA" \
  --hypothesis "短句比长句 impression 高,prompt 收紧到 80 字以内"

之后每 6h 的 Action 自动用新版生成推。一次只改一处,否则分不清哪个改动起的作用。

Step 4 — 攒够样本后判决
bash
python3 scripts/evaluate.py     # 各版本中位 impression + 相邻版本 Δ% 判决

Δ ≥ +10% → keep;Δ ≤ -10% → rollback;之间 → flat。样本不足(<5 条成熟推)显示 measuring。

  • keep:ledger.py keep <SHA> --note "短句 +18%"
  • rollback(先问王建硕):
    bash
    git -C ~/code/wechat-publish checkout <旧SHA> -- prompts/x/prompt.md
    git -C ~/code/wechat-publish commit -m "x prompt: rollback to <旧SHA>"
    再 ledger.py rollback <SHA> --note "短句反而掉了"
Step 5 — 看板
bash
python3 scripts/scoreboard.py    # 写并打印 state/SCOREBOARD.md

现状 + 版本排行榜 + 内容特征(angle)+ to-do。给王建硕看就发这个。


数据模型(state/)

  • tweets.jsonl —— 一推一行:{tweet_id, date, impressions, engagements, likes, replies, reposts, new_follows, char_len, text, slug, angle, source(bot|manual), prompt_sha, age_days, mature}
  • versions.jsonl —— 一 prompt 版本一行:{id, prompt_sha, hypothesis, registered, status(active|kept|rolled_back), verdict, notes}
  • SCOREBOARD.md —— 生成物

默认参数(要改传 flag / 改 _common.py)

  • 成熟窗 MATURITY_DAYS=3
  • 版本判决最少样本 MIN_TWEETS_PER_VERSION=5
  • keep/rollback 阈值 --threshold 0.10
  • 回滚 prompt 永远先问

路径假设(_common.py 顶部,换机器改这里)

  • 发推历史:~/.claude/skills/wjs-tweeting-from-articles/state/history.jsonl
  • prompt 文件:~/code/wechat-publish/prompts/x/prompt.md

© jianshuo, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts) in wjs-x-improving-content of jianshuo/claude-skills.

  • SKILL.md
  • inbox/.gitignore
  • scripts/_common.py
  • scripts/analyze-content.py
  • scripts/evaluate.py
  • scripts/ingest-tweets.py
  • scripts/ledger.py
  • scripts/scoreboard.py
  • state/.gitignore
  • state/SCOREBOARD.md
  • state/tweets.jsonl
  • state/versions.jsonl

Open the folder on GitHubat commit b2690f5

Compare with similar skills

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Podcast Pipelineericosiu/ai-marketing-skills3.6k1 repos~2.6kAutomated safety check: PassMIT
AI Social Media ContentNeverSight/learn-skills.dev2171 repos~1.8kAutomated safety check: PassNone
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Search Xsundial-org/awesome-openclaw-skills663—~599Automated safety check: PassNone

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Works with

Questions about Wjs X Improving Content

What does Wjs X Improving Content do?

A skill your agent uses when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and…. Wjs X Improving Content is an agent skill from jianshuo/claude-skills.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets.

When should I use Wjs X Improving Content?

Wjs X Improving Content fits situations like: 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md; used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets; 什么内容 impression 高; improve my tweets.

How do I install Wjs X Improving Content in Claude Code?

Run `npx skills add jianshuo/claude-skills --skill wjs-x-improving-content -a claude-code`. Or copy the skill folder (wjs-x-improving-content in jianshuo/claude-skills) into .claude/skills/wjs-x-improving-content in your project. Claude Code loads it when a task matches its description.

How do I install Wjs X Improving Content in Codex?

Run `npx skills add jianshuo/claude-skills --skill wjs-x-improving-content -a codex`. Or copy the skill folder (wjs-x-improving-content in jianshuo/claude-skills) into .agents/skills/wjs-x-improving-content in your project. Codex loads it when a task matches its description.

Can I use Wjs X Improving Content in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jianshuo/claude-skills --skill wjs-x-improving-content -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wjs-x-improving-content, .gemini/skills/wjs-x-improving-content, .github/skills/wjs-x-improving-content and .opencode/skills/wjs-x-improving-content in your project.

What does Wjs X Improving Content need to run?

Going by SKILL.md and its folder, Wjs X Improving Content needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and git). Our summary lists: Python 3.

Does Wjs X Improving Content access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Wjs X Improving Content safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Wjs X Improving Content use?

Wjs X Improving Content is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Wjs X Improving Content use?

About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Wjs X Improving Content?

Skills that share tags, products or a category with Wjs X Improving Content: Hook Writer Sms (blacktwist/social-media-skills, 560 stars), Podcast Pipeline (ericosiu/ai-marketing-skills, 3.6k stars), AI Social Media Content (NeverSight/learn-skills.dev, 217 stars) and Download X Video (swyxio/skills, 176 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Wjs X Improving Content?

jianshuo (a GitHub user) maintains it in jianshuo/claude-skills, which has 131 GitHub stars. The repository holds 38 skills in this directory. The repository was last updated on August 20, 2026.

Source: jianshuo/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.