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 yanhua1010/self-media-content-workflow --skill self-media-content-analyticsgit clone --depth 1 https://github.com/yanhua1010/self-media-content-workflowWrote 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/yanhua1010/self-media-content-workflow/self-media-content-analytics)<a href="https://agentmods.dev/skills/yanhua1010/self-media-content-workflow/self-media-content-analytics"><img src="https://agentmods.dev/badge/skills/yanhua1010/self-media-content-workflow/self-media-content-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/yanhua1010/self-media-content-workflow/self-media-content-analytics"><img src="https://agentmods.dev/badge/skills/yanhua1010/self-media-content-workflow/self-media-content-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 | $0.00102 | $0.00718 |
| Opus 5 | $0.00051 | $0.00359 |
| Sonnet 5 | $0.00020 | $0.00144 |
| Haiku 4.5 | $0.00010 | $0.00072 |
Grade A, and why
self-media-content-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 12d 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.
What it actually says
内容数据复盘
数据来源
优先使用：
- 用户提供的平台后台截图和导出文件。
- 已认证连接器或用户自有账号的只读统计接口。
- 内容任务卡、注册表和历史复盘。
- 公开内容链接，仅用于公开指标和结构观察。
不要估算平台没有提供的数据。读取自有账号数据不等于授权修改账号或发布内容。
需要持续记录时，从 metrics-ledger-template.md 创建原始指标台账。若项目已有数据库、表格或分析系统，继续使用现有系统，不重复建账。
分析流程
1. 校验数据
检查平台、内容、发布日期、观察窗口、字段定义、缺失值和异常值。区分曝光、阅读或播放、互动、关注、转化和制作成本。
2. 给出核心结论
说明表现相对自身基线如何、最值得注意的信号是什么，以及哪些结论不能成立。
3. 做归因
按证据强弱检查：
- 选题和目标受众。
- 标题和封面。
- 开头 3 秒或第一屏。
- 结构、证据和信息密度。
- 发布时间、标签、合集和行动。
- 热点、投流、商单和账号体量等外部因素。
相关性不等于因果。没有对照或样本不足时写“待验证”。
4. 做同类比较
只比较同平台、同内容类型和相近时间窗口。优先使用中位数、P75、每千浏览新关注、深度互动率和制作时间。跨平台原始播放量不能直接排名。
完整指标定义见 metrics.md。
5. 形成决策和实验
结论归入：加码、改包装、改主页或系列、平台再适配、停止、样本不足。
每次只设计一个主要实验变量，写清假设、改动、成功标准和观察窗口。
复盘层级
- 单篇复盘：使用 content-review-template.md。
- 周复盘：使用 weekly-review-template.md。
- 月复盘：使用 monthly-review-template.md。
输出
交付：
- 核心结论。
- 数据质量和基线说明。
- 归因及证据强度。
- 可复制因素和不可归因因素。
- 3 到 5 条可执行动作。
- 待验证假设和唯一实验。
商单与自然内容分开分析。样本不足时不调整长期内容比例。
What ships with it
6 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.
- 12d ago First seen · 74 lines · 102 tokens per session scan A bf71558d49db
self-media-content-analytics is a skill published in the GitHub repository yanhua1010/self-media-content-workflow (480 stars, last pushed 19d ago), licensed MIT. It adds 102 tokens to every session and 718 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 2026-08-30.
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