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- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 需要 · Vendor-specific
- 兼容的系统
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- 底层运行要求
- 无特殊要求
- 文件与系统权限
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- 只读
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- 网络行为
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- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-analytics
description: Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize acro…
category: AI 智能
runtime: 无特殊运行时
---
# agent-analytics 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“When to Use This Skill / Philosophy / First-time setup”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“When to Use This Skill / Philosophy / First-time setup”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、会按任务需要访问外部网络、需要准备 Vendor-specific API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件;会按任务需要访问外部网络;需要准备 Vendor-specific API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件。
先用一个小任务确认它会围绕“When to Use This Skill / Philosophy / First-time setup”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: agent-analytics
description: Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize acro…
category: AI 智能
source: davepoon/buildwithclaude
---
# agent-analytics
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「When to Use This Skill / Philosophy / First-time setup」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件;会按任务需要访问外部网络;需要准备 Vendor-specific API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "agent-analytics" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> When to Use This Skill / Philosophy / First-time setup
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件 | 会按任务需要访问外部网络
安全层 -> 需要准备 Vendor-specific API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Agent Analytics — Analytics your agent can actually use
You are adding analytics tracking using Agent Analytics — the analytics platform your AI agent can actually use. Built for developers who ship lots of projects and want their AI agent to track, analyze, experiment, and optimize across all of them.
Website: agentanalytics.sh GitHub: Agent-Analytics/agent-analytics Docs: docs.agentanalytics.sh
When to Use This Skill
- User wants to add analytics tracking to a website or app
- User wants to check how their projects are doing (traffic, conversions, engagement)
- User wants to run A/B experiments on headlines, CTAs, or flows
- User wants funnel analysis, retention cohorts, or traffic breakdowns
- User asks "how's my site doing?" or "are people visiting?"
Philosophy
You are NOT Mixpanel. Don't track everything. Track only what answers: "Is this project alive and growing?"
For a typical site, that's 3-5 custom events max on top of automatic page views.
First-time setup
Get an API key: Sign up at agentanalytics.sh and generate a key from the dashboard. Alternatively, self-host the open-source version from GitHub.
If the project doesn't have tracking yet:
# 1. Login (one time — uses your API key)
npx @agent-analytics/cli login --token aak_YOUR_API_KEY
# 2. Create the project (returns a project write token)
npx @agent-analytics/cli create my-site --domain https://mysite.com
# 3. Add the snippet using the returned token
# 4. Deploy, click around, verify:
npx @agent-analytics/cli events my-site
The create command returns a project write token — use it as data-token in the snippet. This is separate from your API key (which is for reading/querying).
Step 1: Add the tracking snippet
The create command returns a tracking snippet with your project token — add it before </body>. It auto-tracks page_view events with path, referrer, browser, OS, device, screen size, and UTM params. You do NOT need to add custom page_view events.
Step 1b: Discover existing events (existing projects)
If tracking is already set up, check what events and property keys are already in use so you match the naming:
npx @agent-analytics/cli properties-received PROJECT_NAME
Step 2: Add custom events to important actions
Use onclick handlers on the elements that matter:
<a href="..." onclick="window.aa?.track('EVENT_NAME', {id: 'ELEMENT_ID'})">
Standard events for 80% of SaaS sites
Pick the ones that apply. Most sites need 2-4:
| Event | When to fire | Properties |
|---|---|---|
cta_click |
User clicks a call-to-action button | id (which button) |
signup |
User creates an account | method (github/google/email) |
login |
User returns and logs in | method |
feature_used |
User engages with a core feature | feature (which one) |
checkout |
User starts a payment flow | plan (free/pro/etc) |
error |
Something went wrong visibly | message, page |
What NOT to track
- Every link or button (too noisy)
- Scroll depth (not actionable)
- Form field interactions (too granular)
- Footer links (low signal)
Property naming rules
- Use
snake_case:hero_get_startednotheroGetStarted - The
idproperty identifies WHICH element: short, descriptive - Name IDs as
section_action:hero_signup,pricing_pro,nav_dashboard
Step 2b: Run A/B experiments
Experiments let you test which variant of a page element converts better. The full lifecycle is API-driven — no dashboard UI needed.
Creating an experiment
npx @agent-analytics/cli experiments create my-site \
--name signup_cta --variants control,new_cta --goal signup
Implementing variants
Declarative (recommended): Use data-aa-experiment and data-aa-variant-{key} HTML attributes. Original content is the control. The tracker swaps text for assigned variants automatically.
<h1 data-aa-experiment="signup_cta" data-aa-variant-new_cta="Start Free Trial">Sign Up</h1>
Programmatic (complex cases): Use window.aa?.experiment(name, variants) — deterministic, same user always gets same variant.
Checking results
npx @agent-analytics/cli experiments get exp_abc123
Returns Bayesian probability_best, lift, and a recommendation. The system needs ~100 exposures per variant before results are significant.
Step 3: Test immediately
After adding tracking, verify it works:
# Click around, then check:
npx @agent-analytics/cli events PROJECT_NAME
# Events appear within seconds.
CLI Reference
All commands use npx @agent-analytics/cli:
# Setup
login --token aak_YOUR_KEY # Save API key (one time)
projects # List all projects
create my-site --domain https://... # Create project
# Real-time
live # Live TUI dashboard across ALL projects
live my-site # Live view for one project
# Analytics
stats my-site --days 7 # Overview: events, users, daily trends
insights my-site --period 7d # Period-over-period comparison
breakdown my-site --property path --event page_view --limit 10 # Top pages/referrers/UTM
pages my-site --type entry # Landing page performance & bounce rates
sessions-dist my-site # Session engagement histogram
heatmap my-site # Peak hours & busiest days
events my-site --days 30 # Raw event log
sessions my-site # Individual session records
properties my-site # Discover event names & property keys
funnel my-site --steps "page_view,signup,purchase" # Funnel drop-off
retention my-site --period week --cohorts 8 # Cohort retention
# A/B experiments
experiments list my-site
experiments create my-site --name signup_cta --variants control,new_cta --goal signup
experiments get exp_abc123
experiments complete exp_abc123 --winner new_cta
Which endpoint for which question
| User asks | Call | Why |
|---|---|---|
| "How's my site doing?" | insights + breakdown + pages (parallel) |
Full weekly picture |
| "Is anyone visiting right now?" | live |
Real-time visitors across all projects |
| "What are my top pages?" | breakdown --property path --event page_view |
Ranked page list |
| "Where's my traffic coming from?" | breakdown --property referrer --event page_view |
Referrer sources |
| "Are people actually engaging?" | sessions-dist |
Bounce vs engaged split |
| "When should I deploy?" | heatmap |
Find low-traffic windows |
| "Where do users drop off?" | funnel --steps "page_view,signup,purchase" |
Step-by-step conversion |
| "Are users coming back?" | retention --period week --cohorts 8 |
Cohort retention |
| "Which CTA converts better?" | experiments create + experiments get |
A/B test lifecycle |
For any "how is X doing" question, always call insights first — it's the single most useful endpoint.
Examples
Track custom events via window.aa?.track():
window.aa?.track('cta_click', {id: 'hero_get_started'});
window.aa?.track('signup', {method: 'github'});
window.aa?.track('feature_used', {feature: 'create_project'});
window.aa?.track('checkout', {plan: 'pro'});
What this skill does NOT do
- No GUI dashboards — your agent IS the dashboard (or use
livefor a real-time TUI) - No user management or billing
- No PII stored — IP addresses are not logged or retained. Privacy-first by design
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核