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-time-impact-attributiongit 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-time-impact-attribution)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-time-impact-attribution"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-time-impact-attribution/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-time-impact-attribution"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-time-impact-attribution.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 | $0.00045 | $0.01925 |
| Opus 5 | $0.00023 | $0.00962 |
| Sonnet 5 | $0.00009 | $0.00385 |
| Haiku 4.5 | $0.00005 | $0.00193 |
Grade A, and why
bi-time-impact-attribution 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-time-impact-attribution
将指标周期波动拆解为结构变动、趋势变动和事件影响，量化各时间因素对指标变化的贡献。典型场景：拆解本周期 vs 上周期的指标变动来源，量化节假日、营销活动、产品发布等事件对指标的影响度。
适用范围：
- 仅适用于累加型量值指标：各时间点的值可直接求和得到周期总量，如 DAU、访问量、GMV、订单量。率值指标（留存率、转化率等）不适用
- 时间粒度：通常适用于周度或月度对比
执行步骤
1：数据准备
需要两份数据：
指标数据：包含本周期和上周期日粒度数据的 CSV 文件，至少包含以下两列：
| 列 | 说明 | 示例 |
|---|---|---|
| 日期列 | 时间标识 | 日期 |
| 指标列 | 指标值 | 访问用户数 |
示例：
日期,访问用户数
2025-01-01,10000
2025-01-02,10500
...
事件信息（可选）：本周期和上周期内发生的事件列表（CSV），包含事件名称和起止日期。不提供则只拆解结构变动和趋势变动，不计算事件影响：
| 列 | 说明 | 示例 |
|---|---|---|
| 事件名称 | 事件描述 | 春节 |
| 开始日期 | 事件起始 | 2025-01-28 |
| 结束日期 | 事件结束 | 2025-02-04 |
示例：
事件名称,开始日期,结束日期
春节,2025-01-28,2025-02-04
元旦,2025-01-01,2025-01-01
若上游步骤已产出可用数据文件则直接使用，否则自行取数并按上述格式组织。若未提供事件信息，跳过步骤 2 和 3，直接进入步骤 4。
2：确定事件影响时间区间
每个事件需确定其影响的时间区间：
- 已指定：若外部已提供事件影响区间，按以下优先级取值，命中即停：
优先级 来源 示例 1 用户显式指定 用户要求"春节影响区间为 1.25-2.05" 2 域知识包（若存在） 域知识包指定各事件的影响区间 3 语义层接口（若可用） 通过接口查询到的事件影响区间配置 - 未指定：若没有外部来源提供，按以下默认规则推断：
事件类型 时间区间范围 默认值 节假日 假期开始前 N 天 ~ 假期结束 N=1 事件波动 事件开始当天 ~ 事件开始后 M 天 M=2
3：确定事件影响度阈值
事件影响度阈值用于计算指标基线：影响度超过阈值的事件视为"大事件"，其事件日将被剔除后计算基线，以获得更准确的非事件日基线水平。低于阈值的"小事件"不剔除。
- 已有阈值：若外部已提供阈值，按以下优先级取值，命中即停：
优先级 来源 示例 1 用户显式指定 用户要求"影响度阈值设为 5%" 2 域知识包（若存在） 域知识包指定事件影响度阈值为 10% 3 语义层接口（若可用） 通过接口查询到的阈值配置 - 未提供阈值：若没有任何外部来源提供阈值，尝试从历史事件影响度数据中自适应计算阈值
4：执行时间归因
按以下优先级选择计算方式，命中即停：
方式一：使用脚本
路径：scripts/time_impact_attribution.py
原理：先排除事件干扰建立纯净指标基线，再将总变动拆解为结构变动、趋势和事件影响三部分：
What ships with it
1 file 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 · 164 lines · 45 tokens per session scan A 233a331069fa
bi-time-impact-attribution is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (73 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 1,925 once invoked, about $0.0002 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.
Other skills, from other repositories
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
status
Show whether Mem0 memory is working in this repository, covering configuration, capture state, pending flushes, and whether the Mem0 API key is valid. Use when the user asks whether memory is on, why a memory is missing, or anything looks broken.
pause
Pause Mem0 memory capture on this machine. Use when the user wants to stop memories being recorded, for example for private work or experiments.
open-source
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…
mem0-test-integration
Verify a Mem0 integration produced by /mem0-integrate. Runs in the same workspace on the same branch (loose coupling) — installs dependencies, runs the repo's native test suite, then exercises a real end-to-end smoke flow against the user's API key. Produces a scorecard. TRIGGER when: user has just run /mem0-integrate…
connect-recommend
Use this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue…