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 agentmods add skills/zju-real/easel/skill-data-trackernpx skills add ZJU-REAL/Easel --skill skill-data-trackergit clone --depth 1 https://github.com/ZJU-REAL/EaselWrote 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/zju-real/easel/skill-data-tracker)<a href="https://agentmods.dev/skills/zju-real/easel/skill-data-tracker"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-data-tracker.svg" alt="Measured on agentmods" 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 | $0.00145 | $0.02224 |
| Opus 5 | $0.00072 | $0.01112 |
| Sonnet 5 | $0.00029 | $0.00445 |
| Haiku 4.5 | $0.00015 | $0.00222 |
Grade A, and why
skill-data-tracker 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 5d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
社媒数据记录与趋势分析
记录社媒指标快照、分析粉丝增长趋势、追踪内容生命周期，用时间序列数据驱动运营决策。
数据层定位
本 SKILL 是归因链的粉丝 / 时序快照底座，唯一权威存储粉丝数、互动量、内容生命周期的时间序列快照（outputs/_analytics/snapshots/{profile}/{platform}/{date}.json）。
- 只存时序快照，不存发布事件 — 每次发布的元信息（标题 / 链接 / 类型 / 来源 SKILL）由
skill-publish-log维护（outputs/_analytics/publish-log.json）。本底座不重复记录发布事件，避免同一事实两处存储。 - 消费方（读，不回写） —
skill-publish-analytics模式 D（增长归因）与skill-social-performance-review（环比 / 粉丝趋势）以本快照为粉丝时序的权威来源。
| 底座 | 存什么 | 谁维护 |
|---|---|---|
outputs/_analytics/snapshots/{profile}/{platform}/{date}.json |
粉丝 / 互动时序快照（本 SKILL） | skill-data-tracker |
outputs/_analytics/publish-log.json |
发布事件 | skill-publish-log |
输入
| 字段 | 必填 | 说明 |
|---|---|---|
| mode | 是 | record / growth / lifecycle |
| platform | Mode A: 是 | 平台名（小红书/抖音/微博/B站/公众号等） |
| followers | Mode A: 是 | 当前粉丝数 |
| total_likes | Mode A: 否 | 总获赞数 |
| total_posts | Mode A: 否 | 总笔记/视频数 |
| post_snapshots | Mode A: 否 | 近期帖子的逐条数据（用于生命周期追踪） |
| post_title | Mode C: 是 | 要追踪的帖子标题或标识 |
| time_range | Mode B: 否 | 分析窗口（默认近 30 天） |
输出
Mode A — 记录快照
# 数据快照记录
- 日期: {date} | 平台: {platform} | Profile: {profile_name}
## 账号指标
| 指标 | 当前值 | 上次记录 | 变化 |
|------|--------|---------|------|
## 帖子快照（如有）
| 标题 | 发布日期 | 点赞 | 收藏 | 评论 | 转发 |
快照已保存至: outputs/_analytics/snapshots/{profile}/{platform}/{date}.json
Mode B — 增长趋势
# 增长趋势分析
- 平台: {platform} | 区间: {start} → {end} | 数据点: {count}
## 粉丝增长趋势
| 日期 | 粉丝数 | 日增长 | 日增长率 |
## 关键指标
- 日均/周均增长 | 趋势方向: 加速/稳定/减速
- 最高/最低单日增长
- 里程碑预测: 照此速度，{X} 天后破 {milestone} 粉
- 趋势洞察: {增长加速/减速原因分析与建议}
Mode C — 内容生命周期
# 内容生命周期分析
- 帖子: {post_title} | 发布: {published_at} | 平台: {platform}
## 生命周期数据
| 天数 | 日期 | 点赞 | 收藏 | 评论 | 转发 | 日增量 |
（Day 0 / 1 / 3 / 7 / 14 / 30 各行）
## 分类与洞察
- 类型: 速爆型/稳增型/长尾型 | 峰值日: Day {peak} | 半衰期: {days} 天
- 判定依据与后续策略启示
数据存储
快照文件路径：outputs/_analytics/snapshots/{profile}/{platform}/{date}.json
{
"date": "2026-07-22",
"platform": "xiaohongshu",
"profile": "科技数码达人",
"account_metrics": {
"followers": 5200,
"total_likes": 42000,
"total_posts": 89
},
"post_snapshots": [
{
"post_id": "用户提供或自动编号",
"title": "帖子标题",
"published_at": "2026-07-20",
"likes": 350,
"collects": 120,
"comments": 28,
"shares": 15
}
]
}
What ships with it
2 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.
- 5d ago First seen · 190 lines · 145 tokens per session scan A ecc48db697ae
skill-data-tracker is a skill published in the GitHub repository ZJU-REAL/Easel (352 stars, last pushed yesterday), licensed Apache-2.0. It adds 145 tokens to every session and 2,224 once invoked, about $0.0007 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 2026-08-30.
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