Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add EasifyCoder/yuhao-skills --skill yh-x-tweet-analyzergit clone --depth 1 https://github.com/EasifyCoder/yuhao-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/easifycoder/yuhao-skills/yh-x-tweet-analyzer)<a href="https://agentmods.dev/skills/easifycoder/yuhao-skills/yh-x-tweet-analyzer"><img src="https://agentmods.dev/badge/skills/easifycoder/yuhao-skills/yh-x-tweet-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/easifycoder/yuhao-skills/yh-x-tweet-analyzer"><img src="https://agentmods.dev/badge/skills/easifycoder/yuhao-skills/yh-x-tweet-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.03422 |
| Opus 5 | $0.00041 | $0.01711 |
| Sonnet 5 | $0.00016 | $0.00684 |
| Haiku 4.5 | $0.00008 | $0.00342 |
Grade A, and why
yh-x-tweet-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
yh-x-tweet-analyzer
推文分析三件套：HTML 可视化 + 风格画像 + 基于画像的推文生成。
Before Starting
- 读取
${YUHAO_SKILL_DIR}/references/hook-keywords.md（钩子分类规则） - 读取
${YUHAO_SKILL_DIR}/references/topic-keywords.md（选题分类规则） - 读取
${YUHAO_SKILL_DIR}/references/profile-template.md（风格画像模板） - 确认输入 JSON 存在且非空（模式 A/B 需要）
- 确认 styles/ 目录已存在可用画像（模式 C 需要）
模式路由
根据用户意图自动选择模式：
| 用户说了什么 | 模式 | 走哪个流程 |
|---|---|---|
| "分析推文""生成 HTML 报告" | A：HTML 可视化 | → Step 1-4 |
| "生成风格画像""提取写作风格" | B：风格画像 | → Step 1-2 + Step 5 |
| "分析 + 画像" 或首次分析 | A+B：全量 | → Step 1-5 |
| "用 XX 风格写推文""按风格生成""写推文" | C：推文生成 | → Step 6 |
Step 1：定位输入
扫描 ~/.yuhao-skills/yh-x-tweet-exporter/*/ 目录，列出可用的导出结果。
如果用户未指定，使用最新的一次导出。
确定参数：
| 参数 | 默认值 |
|---|---|
INPUT_JSON |
导出目录下的 *.json 文件 |
OUTPUT_HTML |
${YUHAO_SKILL_DIR}/html/{username}/{username}-tweet-analysis.html |
TOP_N_VIRAL |
15 |
TOP_N_PER_GROUP |
50 |
完成标记：INPUT_JSON 路径确认
Step 2：数据处理
运行数据处理脚本，对每条推文执行：
- 过滤：移除纯转推（
is_retweet=true），保留带评论的引用推文 - 钩子分类：按
references/hook-keywords.md的关键词规则打标签 - 选题分类：按
references/topic-keywords.md的关键词规则打标签 - 统计计算：月度汇总、爆款排名、选题分组、钩子分组
node "${YUHAO_SKILL_DIR}/scripts/build-analysis.mjs" \
--input "$INPUT_JSON" \
--output "/tmp/tweet-analysis-data.json" \
--top-n-viral "$TOP_N_VIRAL" \
--top-n-per-group "$TOP_N_PER_GROUP"
完成标记：/tmp/tweet-analysis-data.json 生成成功
Step 3：生成 HTML（模式 A）
运行 HTML 生成脚本，从处理后的数据生成自包含暗色主题 HTML：
node "${YUHAO_SKILL_DIR}/scripts/gen-html.mjs" \
--data "/tmp/tweet-analysis-data.json" \
--output "$OUTPUT_HTML"
完成标记：HTML 文件生成，大小 > 10KB
Step 4：打开预览（模式 A）
open "$OUTPUT_HTML"
完成标记：浏览器已打开页面
Step 5：生成风格画像（模式 B）
基于 Step 2 的统计数据 + 高赞推文样本，生成博主的结构化风格画像。
5.1 准备数据
从 /tmp/tweet-analysis-data.json 提取：
- 月度统计、钩子分布、选题分布
- 高赞 Top 20 推文全文
- 各钩子类型代表推文各 3-5 条（含 X 链接）
从原始 INPUT_JSON 提取完整推文文本用于语言分析。
5.2 分析并生成 profile.md
按 references/profile-template.md 的 8 维度模板，逐维度分析：
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 323 lines · 81 tokens per session scan A 5e5dfed2046a
yh-x-tweet-analyzer is a skill published in the GitHub repository EasifyCoder/yuhao-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 3,422 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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