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Agent Skill · 研究检索

product-tracking-generate-implementation-guide(product tracking generate 实现指南)

product-tracking-generate-implementation-guide 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

product-tracking-generate-implementation-guide 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 66,030。

总安装 269周安装 11GitHub Stars 30下载 86核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:product-tracking-generate-implementation-guide(product tracking generate 实现指南)
来源仓库:https://github.com/accoil/product-tracking-skills
仓库路径:skills/product-tracking-generate-implementation-guide
安装命令:
npx skills add https://github.com/accoil/product-tracking-skills --skill product-tracking-generate-implementation-guide
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/accoil/product-tracking-skills --skill product-tracking-generate-implementation-guide

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过 npx skills add 命令从指定仓库安装,需结合原始 README 核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

product-tracking-generate-implementation-guide 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/product-tracking-generate-implementation-guide

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/accoil/product-tracking-skills --skill product…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

product-tracking-generate-implementation-guide 怎么安装?

在终端执行 npx skills add https://github.com/accoil/product-tracking-skills --skill product-tracking-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

product-tracking-generate-implementation-guide 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

product-tracking-generate-implementation-guide 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

product-tracking-generate-implementation-guide 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

product-tracking-generate-implementation-guide 用的人多吗?

公开数据是累计安装 269 次,近一周安装 11 次,下载 86 次,GitHub 30 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

product-tracking-generate-implementation-guide 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
product-tracking-generate-implementation-guide

product tracking generate 实现指南 · 本页

26930接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Instrument

You are a product telemetry engineer producing an SDK-specific instrumentation guide. You show how to make the three core analytics calls — identify, group, track — using the target SDK, with real template code and group hierarchy guidance.

This is a how-to guide, not a catalog. You don't repeat every event from the tracking plan. You show the patterns, the SDK syntax, and the constraints — then the implementation phase applies those patterns to the full event list.

References

Read the matching destination reference before producing anything. References are organized by category.

Product Analytics & CDPs (full identify → group → track support)

B2B Engagement Platforms

Full-Stack & Web Analytics

Error & Performance Monitoring

Feature Flags & Experimentation

Session & Behavior Tools

Tag Management

Platform & Architecture

Not every destination supports the full identify → group → track model. Web analytics tools (Plausible, Fathom, etc.) track page views and simple events only. Error monitoring tools (Sentry, New Relic) provide user context for debugging, not product analytics. Feature flag tools (LaunchDarkly, Statsig) manage targeting context and experiment exposure. Each reference file documents what the tool supports and what it doesn't.

Supporting Files

Goal

Produce .telemetry/instrument.md — an SDK-specific guide covering the three core calls (identify, group, track) with real template code. This file becomes the contract that the implementation phase follows.

Prerequisites

Check before starting:

  1. .telemetry/tracking-plan.yaml (required) — The tracking plan defines entities, groups, and events. If it doesn't exist, stop and tell the user: *"I need a tracking plan to generate instrumentation guidance. Run the product-tracking-design-tracking-plan skill first to create one (e.g., 'design tracking plan')."*
  2. .telemetry/current-implementation.md (optional) — If this file exists (from the audit skill), read it for context on the existing SDK setup.

Inputs

  1. Target plan (.telemetry/tracking-plan.yaml) — entity traits, group hierarchy, meta
  2. Delta plan (.telemetry/delta.md) — what needs to change (if available)
  3. Target SDK — confirmed with the user
  4. Current implementation (.telemetry/current-implementation.md) — if the audit populated this file, read it for context

Process

0. Verify Reference Access

Always do this first. Read each of the following reference files and output a one-line confirmation for each (filename + first heading or one-line summary). This confirms you can access the skill's supporting files:

Then read the SDK reference matching the user's target (from the References list above) and confirm it the same way.

If any file cannot be read, tell the user immediately — do not proceed without the references.

1. Determine Target SDK (Inherit Before Asking)

Check upstream artifacts before asking the user:

  • Read .telemetry/tracking-plan.yaml meta: block for destinations and platform
  • Read .telemetry/product.md Integration Targets section for analytics destinations

If the target SDK is already established in these artifacts, confirm it briefly ("The tracking plan targets Accoil via Forge — proceeding with that.") and move on. Only ask "Which SDK should this instrumentation guide target?" if no upstream context exists.

Context inheritance: Read upstream artifacts first. Present what you found as confirmation: "From the product model, I see this is a [language/framework] codebase targeting [destinations]. The audit shows [existing patterns]. Proceeding with that context." Only ask if something is missing.

Hard gate: You MUST read the matching SDK reference file before producing anything. If Forge, also read which analytics provider Forge feeds into (from meta.destinations), then read BOTH the forge-platform reference AND the destination SDK reference.

Multi-Destination Guidance

If the tracking plan's meta.destinations lists multiple analytics destinations:

  • Recommend a CDP (Segment, RudderStack) as the single ingestion layer. This means one set of track/identify/group calls in the codebase, routed to all destinations by the CDP.
  • If a CDP is already in use, the instrumentation guide targets the CDP's SDK only. Note destination-specific constraints (e.g., "Accoil receives events via Segment but ignores event properties").
  • If no CDP and multiple direct integrations are needed, produce separate sections per destination but warn about the maintenance burden.

Language Awareness

The instrumentation guide must match the user's technology stack:

  • TypeScript/JavaScript (Node.js): Full type safety with interfaces, union types, typed wrapper functions. Use the SDK's native npm package.
  • Ruby, Python, Go, Java, etc.: No TypeScript types available. Use the generic HTTP architecture (see references/generic-http-architecture.md) with language-appropriate equivalents: Ruby modules, Python dataclasses, Go structs.
  • Frontend-only (browser): If no backend exists, the frontend can call analytics SDKs directly. But prefer backend routing for API key security.

The core principle is always the same: centralized event definitions, async delivery, no scattered raw strings. The implementation language changes; the architecture doesn't.

2. Review Current Implementation (if exists)

If .telemetry/current-implementation.md exists (from the product-tracking-audit-current-tracking skill), read it. Compare against SDK best practices — note what works and what needs to change.

3. Scan for Existing Conventions

Before designing the implementation architecture, scan the codebase — but be targeted. Don't explore broadly. Focus on:

  1. Routes/endpoints — How the app handles requests (routes file, controllers directory)
  2. 2-3 key models/services — The core entities from the product model (e.g., User, Account, Report)
  3. Dependency manifest — What libraries are already available (Gemfile, package.json, requirements.txt, go.mod)
  4. Background job infrastructure — Is there an existing job system? (search for Sidekiq, Celery, Bull, etc. in the dependency manifest)
  5. Existing analytics calls — Any current tracking to understand patterns (from .telemetry/current-implementation.md if available)

From these, extract the conventions the telemetry implementation should follow:

  • HTTP client: What the codebase already uses for outbound calls (Faraday, requests, fetch, Net::HTTP, axios)
  • Background jobs: The existing job system, if any (Sidekiq, Celery, Bull, Delayed::Job, Cloud Tasks)
  • Module organization: How shared code is structured (Rails concerns, Python mixins, TS barrel exports, Go packages)
  • Configuration: How env vars and secrets are managed (dotenv, Rails credentials, Vault)

Design the telemetry implementation to follow these exact conventions. Don't introduce new patterns when existing ones work. If the codebase uses Sidekiq for background jobs, use Sidekiq for analytics delivery. If it uses Faraday for HTTP, use Faraday.

4. Load Tracking Plan

Read .telemetry/tracking-plan.yaml for entity traits, group hierarchy, and meta. You do NOT need to extract every event — the tracking plan tells you the shape of your identify and group calls.

5. Map identify()

SDK-specific syntax, user traits from the plan, when to call, one template example.

6. Map group()

SDK-specific syntax, group hierarchy mapping (how each plan level maps to the SDK), group traits, when to call, one template example per group level. For multi-level hierarchies, explain how the SDK handles nesting (or doesn't).

7. Map track()

SDK-specific syntax, SDK constraints (e.g., Accoil: no event properties), 1-2 representative template examples. Do NOT map every event.

8. Document Group-Level Attribution

How to track events at different group levels in this SDK. Two critical requirements:

a. Every group level needs a group() call. If the hierarchy is account > workspace > project, issue group() for each level with parent_group_id traits to establish the hierarchy. Groups must exist before events reference them.

b. Every track() call needs group context. The tracking plan assigns each event a group_level. The track call must include the group ID for that level. Show the SDK-specific pattern:

  • Segment: context: {groupId: 'proj_123'} in the track call's options object.
  • Amplitude: groups: {project: 'proj_123'} in the track call's options object, or via Segment destination options.
  • Mixpanel: $groups: {project: 'proj_123'} as a property in the track call.
  • PostHog: $groups: {project: 'proj_123'} as a property (browser), or groups: {project: 'proj_123'} in the capture options (Node.js).
  • Accoil: context: {groupId: 'proj_123'} on the track call (tracker.js, Direct API, or via Segment).

Also document:

  • How the SDK associates events with groups (automatic vs explicit)
  • How to attribute to a specific level (account vs workspace vs project)
  • Whether the SDK supports multiple concurrent group contexts
  • Template code showing attribution at different levels

System support for group hierarchy:

  • Strong: Segment (native group calls, hierarchical), Mixpanel (group analytics with multiple group types), PostHog (group analytics)
  • Moderate: Amplitude (group identify, but limited hierarchy depth)
  • Weak: GA4 (no native group concept), basic HTTP-only destinations

If the tracking plan uses multi-level groups and the target system has weak support, note the gap and suggest workarounds (e.g., flattening group traits onto events as properties).

9. Document Architecture

Initialization, client vs server routing, shutdown/flush, error handling, env vars, SDK-specific constraints. For Forge: cover the full architecture (frontend invoke → resolver → queue → consumer → dispatcher).

10. Verification

Include in the instrumentation guide:

  • How to confirm events are arriving at the destination (debug console, API response logs, destination dashboard)
  • Expected delivery latency (real-time for direct API, batched for queue-based)
  • What a successful vs failed delivery looks like (HTTP status codes, error responses)
  • How to test in development without polluting production data (separate API keys, environment flags, dry-run mode)

11. Rollout Strategy

Ask the user: "Do you want a phased rollout (lifecycle events first, then core value) or everything at once?" If phased, include:

  1. Development: Enable verbose/debug logging. Verify event shape and delivery.
  2. Staging: Full integration with real destination using a separate project/source/API key.
  3. Production — gradual: Start with lifecycle events only. Verify in the destination dashboard. Then enable core value events.
  4. Monitoring: Watch for delivery failures, unexpected volume spikes, or missing events in the first week.

If the user wants everything at once, skip the phased plan and just include the verification section.

12. Produce instrument.md

Read references/output-template.md for the output structure. Write the guide to .telemetry/instrument.md. If .telemetry/current-implementation.md exists, reference it in the guide's preamble.

Read references/behavioral-rules.md before finalizing — these are your quality checks.

Key Quality Rules

These are non-negotiable (full rules in references/behavioral-rules.md):

  1. Patterns, not catalogs. Show 1-2 representative examples per call type. Do NOT repeat every event from the tracking plan — that is the implementation phase's job.
  2. Real SDK syntax. Every code block must use the SDK's actual API — correct imports, correct method signatures, correct authentication. Copy patterns from the reference file, do not guess or use generic patterns.
  3. Complete, copy-paste-ready code. instrument.md must contain complete module/class code — not fragments that require mental assembly. The developer should be able to copy a full tracking module (with identify, group, track functions, delivery infrastructure, and event constants) directly into their codebase. Include all imports, class definitions, and initialization — not "add this snippet here."
  4. Single delivery job. Don't over-engineer with separate job classes per call type (e.g., IdentifyJob, GroupJob, TrackJob). Use one delivery job with a method/action parameter: DeliveryJob.perform(method: 'identify', payload: {...}). Simpler, easier to maintain.
  5. Write to file, summarize in conversation. The full guide goes in .telemetry/instrument.md. Show only a concise summary in chat (target SDK, key patterns, constraints, coverage gaps). Never paste more than 20 lines into the conversation.

Lifecycle

model → audit → design → guide → implement ← feature updates
                           ^

Next Phase

After generating the instrumentation guide, suggest the user run:

  • product-tracking-implement-tracking — generate real instrumentation code from this guide (e.g., *"implement tracking"*, *"generate code"*, *"create tracking module"*)

平台分布

Codex

32.36%
按下载量换算28

Claude

31.89%
按下载量换算27

Cursor

17.06%
按下载量换算15

Gemini CLI

9.39%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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