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 agentscope-ai/QwenPaw-Data --skill bi-metric-observationgit clone --depth 1 https://github.com/agentscope-ai/QwenPaw-DataWrote 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/agentscope-ai/qwenpaw-data/bi-metric-observation)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-metric-observation"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-metric-observation/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/agentscope-ai/qwenpaw-data/bi-metric-observation"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-metric-observation.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 | $0.00060 | $0.01503 |
| Opus 5 | $0.00030 | $0.00751 |
| Sonnet 5 | $0.00012 | $0.00301 |
| Haiku 4.5 | $0.00006 | $0.00150 |
Grade A, and why
bi-metric-observation 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-metric-observation
获取指标的原始数据并完成基础计算，构建指标的当期值、变动幅度和维度交叉展示表，为后续分析提供数据基础。常见场景：
- 指标分析：在分析流程中，对指标进行基础数据获取和计算
- 数据概览：快速了解指标当前水平与变动趋势
前置检查
开始前确认以下信息已明确：
- 分析条目明确，清楚本次要观测哪些指标
- 指标与角色已确定，知道每个指标是北极星、展示还是分布角色
- 关联维度已确定，知道北极星指标的归因拆解维度、展示指标的展示拆解维度
- 数据获取能力可用，有可以获取数据的工具或 API
若以上信息不完整，需先回到规划阶段补充，或向用户确认后再开始。
执行步骤
1：数据准备
根据分析目标获取指标的原始数据。数据文件至少包含以下列：
| 列 | 说明 | 示例 |
|---|---|---|
| 日期列 | 时间标识 | 日期 |
| 指标列 | 各指标值，每个指标一列 | 访问用户数 |
| 维度列 | 各维度值，每个维度一列 | 端类型 |
若上游步骤已产出可用数据文件则直接使用，否则自行取数。
2：确定分析时间周期
根据分析目标选择合适的分析时间区间：
| 分析目标 | 时间区间 |
|---|---|
| 日数据波动 | 当日 vs 上日 |
| 日数据波动（消除周末效应） | 当日 vs 上周同日 |
| 周数据波动 | 本周 vs 上周 |
| 月数据波动 | 本月 vs 上月 |
| 年度趋势 | 当期 vs 上年同期 |
| 特定日期分析 | 指定日期前后日/周 |
确定时间区间后，获取该区间内所有指标的原始数据。
3：确定指标计算方式
根据指标类型和时间周期，判断该指标在对应周期下的聚合方式。通用规则：
- 累加型指标（如收入、订单量、新增用户数）：使用周期内聚合值（求和）
- 水平型指标（如日活跃用户数 DAU、活跃率、转化率）：使用周期内均值
- 用户规模型指标（如月活跃用户数 MAU、累计用户数）：使用周期末快照值（去重值或最后一天值）
4：计算衍生指标
根据分析场景计算合适的衍生指标，不需要全部计算：
| 衍生指标 | 适用场景 |
|---|---|
| 日环比 | 日数据，对比日数据波动 |
| 周同比 | 日数据，对比日数据波动，消除周末效应 |
| 周环比 | 周数据，对比周波动 |
| 月环比 | 月数据，对比月波动 |
| 年同比（日） | 日数据，长期业务且有年度周期性波动时关注 |
| 年同比（月） | 月数据，需要年度比较的业务（如 B 端收入） |
| 年环比 | 年/财年数据，对比财年累计值，适用可累加指标（如年收入） |
| 分布占比 | 量值指标在各维度下的分布拆解，展示核心观测指标时需加上分布占比 |
注意：计算衍生指标时，根据指标类型选择正确的变动表达方式，避免产生"百分比的百分比"等歧义：
| 指标类型 | 识别特征 | 变动表达 | 示例 |
|---|---|---|---|
| 量值指标 | 绝对数值，可直接累加 | 百分比增幅 | DAU 增长 10% |
| 率值指标 | 有分子/分母，数值为百分比 | 绝对值增幅 (pt) | 留存率从 40% 到 42%，+2pt |
| 加工指标 | 由其他指标计算得到，数量级小按率值，否则按量值 | 视业务场景而定 | 人均对话次数、客单价 |
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 · 110 lines · 60 tokens per session scan A 23d0073b7456
bi-metric-observation is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (73 stars, last pushed yesterday), licensed Apache-2.0. It adds 60 tokens to every session and 1,503 once invoked, about $0.0003 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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