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 ZJU-REAL/Easel --skill skill-publish-analyticsgit 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-publish-analytics)<a href="https://agentmods.dev/skills/zju-real/easel/skill-publish-analytics"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-publish-analytics/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/zju-real/easel/skill-publish-analytics"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-publish-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00099 | $0.02364 |
| Opus 5 | $0.00049 | $0.01182 |
| Sonnet 5 | $0.00020 | $0.00473 |
| Haiku 4.5 | $0.00010 | $0.00236 |
Grade A, and why
skill-publish-analytics 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 6d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
发布数据归因分析
读取 publish-log.json,从时间、标签、类型、增长四个维度分析内容表现,输出结构化归因报告。
数据层定位
本 SKILL 是归因链的消费层,只读底座、不新建存储、不回写:
| 数据 | 权威底座 | 维护方 | 本 SKILL 用途 |
|---|---|---|---|
| 发布事件 | outputs/_analytics/publish-log.json |
skill-publish-log | 模式 A/B/C |
| 粉丝 / 时序快照 | outputs/_analytics/snapshots/{profile}/{platform}/{date}.json |
skill-data-tracker | 模式 D 增长归因 |
粉丝时序的权威来源是 skill-data-tracker 的快照底座。 模式 D 读取的 outputs/_analytics/follower-log.json 由 track.py export-followers 确定性导出,不应手工维护。字段映射见 references/follower-log-schema.md。
输入
用户指定分析模式(可组合):
- 模式 A — 最佳发布时间:分析发布时段与互动数据的关系
- 模式 B — 标签效果分析:评估标签对内容表现的影响
- 模式 C — 内容类型对比:按内容类型对比各项指标
- 模式 D — 增长归因:关联发布事件与粉丝增长
未指定模式时默认执行 A + B + C。模式 D 前先运行 python3 skills/openclaw/skill-data-tracker/scripts/track.py export-followers。
数据源
publish-log.json 结构
{
"version": "1.0",
"entries": [{
"id": "唯一标识",
"platform": "xiaohongshu|douyin|bilibili|weibo",
"title": "标题",
"url": "发布链接",
"type": "图文|视频|直播|文章",
"published_at": "ISO 8601 时间戳",
"logged_at": "记录时间",
"initial_metrics": {
"views": null | number,
"likes": null | number,
"comments": null | number,
"shares": null | number
},
"skill_source": "生成该内容的 SKILL",
"profile": "账号画像名",
"tags": ["标签列表"],
"notes": "备注"
}]
}
数据处理规则
- null 指标:排除出该指标的平均值计算,报告覆盖率百分比
- 样本量警告:单桶 < 5 条时标注
⚠ 样本不足;全量 < 10 条时在报告头部警告结果可能不具统计意义 - 时区:有 Profile 时使用 Profile 中的时区,无 Profile 时默认 Asia/Shanghai
执行步骤
四种分析模式的全部计算(时段分桶、标签聚合、类型对比、增长归因、样本量警告、
覆盖率)由 scripts/analyze.py 确定性完成。不要用内联 Python 心算,直接调脚本。
LLM 只负责选模式、按 Profile 过滤、解读 JSON、写关键发现/方法论/局限性。
- 检查 Profile 上下文(
=== EASEL ACCOUNT PROFILE ===标记),有则取 profile 名。 - 调用脚本(
--profile须放在子命令前;publish-log.json 不存在时脚本友好报错):
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py all
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 time
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 tags
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 types
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 growth
What ships with it
3 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.
- 6d ago First seen · 200 lines · 99 tokens per session scan A b2ea682a95a9
skill-publish-analytics is a skill published in the GitHub repository ZJU-REAL/Easel (710 stars, last pushed today), licensed Apache-2.0. It adds 99 tokens to every session and 2,364 once invoked, about $0.0005 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 2026-09-03.
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