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Agent Skill · 开发

okr-kpi-patterns(OKR KPI 模式)

okr-kpi-patterns 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

okr-kpi-patterns 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 20,882。

总安装 210周安装 9GitHub Stars 160下载 73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:okr-kpi-patterns(OKR KPI 模式)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/okr-kpi-patterns
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill okr-kpi-patterns
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/yonatangross/orchestkit --skill okr-kpi-patterns

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

okr-kpi-patterns 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。通过 npx skills add 命令从指定 GitHub 仓库安装并使用。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/okr-kpi-patterns

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/yonatangross/orchestkit --skill okr-kpi-patter…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

okr-kpi-patterns 怎么安装?

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

okr-kpi-patterns 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。

okr-kpi-patterns 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

okr-kpi-patterns 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。

okr-kpi-patterns 用的人多吗?

公开数据是累计安装 210 次,近一周安装 9 次,下载 73 次,GitHub 160 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。

okr-kpi-patterns 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
okr-kpi-patterns

OKR KPI 模式 · 本页

210160只读未明确通过

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

OKR & KPI Patterns

Frameworks for defining goals, measuring success, and building metrics-driven organizations.

OKR Framework

Objectives and Key Results align teams around ambitious goals with measurable outcomes.

OKR Structure

Objective: Qualitative, inspiring goal
├── Key Result 1: Quantitative measure of progress
├── Key Result 2: Quantitative measure of progress
└── Key Result 3: Quantitative measure of progress

Writing Good Objectives

CharacteristicGoodBad
Qualitative"Delight enterprise customers""Increase NPS to 50"
Inspiring"Become the go-to platform""Ship 10 features"
Time-boundImplied quarterlyVague timeline
AmbitiousStretch goal (70% achievable)Sandbagged (100% easy)

Writing Good Key Results

CharacteristicGoodBad
Quantitative"Reduce churn from 8% to 4%""Improve retention"
Measurable"Ship to 10,000 beta users""Launch beta"
Outcome-focused"Increase conversion by 20%""Add 5 features"
Leading indicators"Weekly active users reach 50K""Revenue hits $1M" (lagging)

OKR Example

## Q1  OKRs

### Objective 1: Become the #1 choice for enterprise teams

**Key Results:**
- KR1: Increase enterprise NPS from 32 to 50
- KR2: Reduce time-to-value from 14 days to 3 days
- KR3: Achieve 95% feature adoption in first 30 days
- KR4: Win 5 competitive displacements from [Competitor]

### Objective 2: Build a world-class engineering culture

**Key Results:**
- KR1: Reduce deploy-to-production time from 4 hours to 15 minutes
- KR2: Achieve 90% code coverage on critical paths
- KR3: Zero P0 incidents lasting longer than 30 minutes
- KR4: Engineering satisfaction score reaches 4.5/5

Leading vs. Lagging Indicators

Understanding the difference is crucial for effective measurement.

Definitions

TypeDefinitionCharacteristics
LeadingPredictive, can be directly influencedReal-time feedback, actionable
LaggingResults of past actionsConfirms outcomes, hard to change

Examples by Domain

Sales Pipeline:
  Leading: # of qualified meetings this week
  Lagging: Quarterly revenue

Customer Success:
  Leading: Product usage frequency
  Lagging: Customer churn rate

Engineering:
  Leading: Code review turnaround time
  Lagging: Production incidents

Marketing:
  Leading: Website traffic, MQLs
  Lagging: Customer acquisition cost (CAC)

The Leading-Lagging Chain

Leading                                           Lagging
─────────────────────────────────────────────────────────►

Blog posts    Website     MQLs      SQLs      Deals     Revenue
published  →  traffic  →  generated → created → closed → booked
   │            │           │          │         │         │
   ▼            ▼           ▼          ▼         ▼         ▼
 Actionable  Actionable   Somewhat   Less      Hard      Result
             (SEO, ads)   (content)  control   control

Using Both Effectively

## Balanced Metrics Dashboard

### Leading Indicators (Weekly Review)
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Active users (DAU) | 12,500 | 15,000 | 🟡 |
| Feature adoption rate | 68% | 75% | 🟡 |
| Support ticket volume | 142 | <100 | 🔴 |
| NPS responses collected | 89 | 100 | 🟢 |

### Lagging Indicators (Monthly Review)
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Monthly revenue | $485K | $500K | 🟡 |
| Customer churn | 5.2% | <5% | 🟡 |
| NPS score | 42 | 50 | 🟢 |
| CAC payback months | 14 | 12 | 🔴 |

KPI Trees

Hierarchical breakdown of metrics showing cause-effect relationships.

Revenue KPI Tree

                         Revenue
                            │
          ┌─────────────────┼─────────────────┐
          │                 │                 │
     New Revenue      Expansion         Retained
          │            Revenue           Revenue
          │                │                 │
    ┌─────┴─────┐    ┌─────┴─────┐    ┌─────┴─────┐
    │           │    │           │    │           │
  Leads ×    Conv  Users ×   Upsell  Existing × (1-Churn)
  Rate       Rate   ARPU      Rate    Revenue     Rate

Product Health KPI Tree

                    Product Health Score
                            │
         ┌──────────────────┼──────────────────┐
         │                  │                  │
    Engagement          Retention         Satisfaction
         │                  │                  │
    ┌────┴────┐       ┌────┴────┐       ┌────┴────┐
    │         │       │         │       │         │
   DAU/     Time    Day 1    Day 30    NPS    Support
   MAU      in App  Retention Retention       Tickets

North Star Metric

One metric that captures core value delivery.

Examples by Business Type

Business TypeNorth Star MetricWhy
SaaSWeekly Active UsersIndicates ongoing value
MarketplaceGross Merchandise ValueCaptures both sides
MediaTime spent readingEngagement = value
E-commercePurchase frequencyRepeat = satisfied
FintechAssets under managementTrust + usage

North Star + Input Metrics

## Our North Star Framework

**North Star:** Weekly Active Teams (WAT)

**Input Metrics:**
1. New team signups (acquisition)
2. Teams completing onboarding (activation)
3. Features used per team per week (engagement)
4. Teams inviting new members (virality)
5. Teams on paid plans (monetization)

**Lagging Validation:**
- Revenue growth
- Net retention rate
- Customer lifetime value

Metric Definition Template

## Metric: [Name]

### Definition
[Precise definition of what this metric measures]

### Formula

Metric = Numerator / Denominator

### Data Source
- System: [Where data comes from]
- Table/Event: [Specific location]
- Owner: [Team responsible]

### Segments
- By customer tier (Free, Pro, Enterprise)
- By geography (NA, EMEA, APAC)
- By cohort (signup month)

### Frequency
- Calculation: Daily
- Review: Weekly

### Targets
| Period | Target | Stretch |
|--------|--------|---------|
| Q1 | 10,000 | 12,000 |
| Q2 | 15,000 | 18,000 |

### Related Metrics
- Leading: [Metric that predicts this]
- Lagging: [Metric this predicts]

Common Pitfalls

PitfallMitigation
Vanity metricsFocus on metrics that drive decisions
Too many KPIsLimit to 5-7 per team
Gaming metricsPair metrics that balance each other
Lagging onlyInclude leading indicators for early signals
No baselinesEstablish current state before setting targets
Static goalsReview and adjust quarterly

Best Practices

  • OKRs for goals, KPIs for health: Use together, not interchangeably
  • Leading indicator focus: Key Results should be leading indicators
  • Cascade with autonomy: Align outcomes, let teams choose their path
  • Regular calibration: Weekly check-ins on leading, monthly on lagging
  • AI-assisted insights: Use AI to detect anomalies and suggest actions

Related Skills

  • product-strategy-frameworks - Strategic context for metrics
  • business-case-analysis - Financial metrics and ROI
  • prioritization-frameworks - Using metrics to prioritize

References

Version: 1.0.0 (January)

平台分布

Codex

35.26%
按下载量换算26

Claude

30.26%
按下载量换算22

Cursor

20.42%
按下载量换算15

Gemini CLI

8.39%
按下载量换算6

安全审计

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通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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