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Agent Skill · 开发

variance-analysis(方差分析)

variance-analysis 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。

variance-analysis 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 1,839。

总安装 23,257周安装 999GitHub Stars 11,694下载 8,152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:variance-analysis(方差分析)
来源仓库:https://github.com/anthropics/knowledge-work-plugins
仓库路径:skills/variance-analysis
安装命令:
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill variance-analysis
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/anthropics/knowledge-work-plugins --skill variance-analysis

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。

variance-analysis 用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息。它适合围绕代码变更、仓库状态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。安装前建议确认权限范围和是否会触发文件读写操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/variance-analysis

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/anthropics/knowledge-work-plugins --skill vari…;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

variance-analysis 怎么安装?

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

variance-analysis 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。

variance-analysis 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。

variance-analysis 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。

variance-analysis 用的人多吗?

公开数据是累计安装 23,257 次,近一周安装 999 次,下载 8,152 次,GitHub 11,694 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。

variance-analysis 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。
  • 来源仓库有 11,694 星,维护活跃度相对可查。
  • 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
variance-analysis

方差分析 · 本页

23,25711,694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

Variance Analysis

Important: This skill assists with variance analysis workflows but does not provide financial advice. All analyses should be reviewed by qualified financial professionals before use in reporting.

Techniques for decomposing variances, materiality thresholds, narrative generation, waterfall chart methodology, and budget vs actual vs forecast comparisons.

Variance Decomposition Techniques

Price / Volume Decomposition

The most fundamental variance decomposition. Used for revenue, cost of goods, and any metric that can be expressed as Price x Volume.

Formula:

Total Variance = Actual - Budget (or Prior)

Volume Effect  = (Actual Volume - Budget Volume) x Budget Price
Price Effect   = (Actual Price - Budget Price) x Actual Volume
Mix Effect     = Residual (interaction term), or allocated proportionally

Verification:  Volume Effect + Price Effect = Total Variance
               (when mix is embedded in the price/volume terms)

Three-way decomposition (separating mix):

Volume Effect = (Actual Volume - Budget Volume) x Budget Price x Budget Mix
Price Effect  = (Actual Price - Budget Price) x Budget Volume x Actual Mix
Mix Effect    = Budget Price x Budget Volume x (Actual Mix - Budget Mix)

Example — Revenue variance:

  • Budget: 10,000 units at $50 = $500,000
  • Actual: 11,000 units at $48 = $528,000
  • Total variance: +$28,000 favorable

- Volume effect: +1,000 units x $50 = +$50,000 (favorable — sold more units) - Price effect: -$2 x 11,000 units = -$22,000 (unfavorable — lower ASP) - Net: +$28,000

Rate / Mix Decomposition

Used when analyzing blended rates across segments with different unit economics.

Formula:

Rate Effect = Sum of (Actual Volume_i x (Actual Rate_i - Budget Rate_i))
Mix Effect  = Sum of (Budget Rate_i x (Actual Volume_i - Expected Volume_i at Budget Mix))

Example — Gross margin variance:

  • Product A: 60% margin, Product B: 40% margin
  • Budget mix: 50% A, 50% B → Blended margin 50%
  • Actual mix: 40% A, 60% B → Blended margin 48%
  • Mix effect explains 2pp of margin compression

Headcount / Compensation Decomposition

Used for analyzing payroll and people-cost variances.

Total Comp Variance = Actual Compensation - Budget Compensation

Decompose into:
1. Headcount variance    = (Actual HC - Budget HC) x Budget Avg Comp
2. Rate variance         = (Actual Avg Comp - Budget Avg Comp) x Budget HC
3. Mix variance          = Difference due to level/department mix shift
4. Timing variance       = Hiring earlier/later than planned (partial-period effect)
5. Attrition impact      = Savings from unplanned departures (partially offset by backfill costs)

Spend Category Decomposition

Used for operating expense analysis when price/volume is not applicable.

Total OpEx Variance = Actual OpEx - Budget OpEx

Decompose by:
1. Headcount-driven costs    (salaries, benefits, payroll taxes, recruiting)
2. Volume-driven costs       (hosting, transaction fees, commissions, shipping)
3. Discretionary spend       (travel, events, professional services, marketing programs)
4. Contractual/fixed costs   (rent, insurance, software licenses, subscriptions)
5. One-time / non-recurring  (severance, legal settlements, write-offs, project costs)
6. Timing / phasing          (spend shifted between periods vs plan)

Materiality Thresholds and Investigation Triggers

Setting Thresholds

Materiality thresholds determine which variances require investigation and narrative explanation. Set thresholds based on:

  1. Financial statement materiality: Typically 1-5% of a key benchmark (revenue, total assets, net income)
  2. Line item size: Larger line items warrant lower percentage thresholds
  3. Volatility: More volatile line items may need higher thresholds to avoid noise
  4. Management attention: What level of variance would change a decision?

Recommended Threshold Framework

Comparison TypeDollar ThresholdPercentage ThresholdTrigger
Actual vs BudgetOrganization-specific10%Either exceeded
Actual vs Prior PeriodOrganization-specific15%Either exceeded
Actual vs ForecastOrganization-specific5%Either exceeded
Sequential (MoM)Organization-specific20%Either exceeded

*Set dollar thresholds based on your organization's size. Common practice: 0.5%-1% of revenue for income statement items.*

Investigation Priority

When multiple variances exceed thresholds, prioritize investigation by:

  1. Largest absolute dollar variance — biggest P&L impact
  2. Largest percentage variance — may indicate process issue or error
  3. Unexpected direction — variance opposite to trend or expectation
  4. New variance — item that was on track and is now off
  5. Cumulative/trending variance — growing each period

Narrative Generation for Variance Explanations

Structure for Each Variance Narrative

[Line Item]: [Favorable/Unfavorable] variance of $[amount] ([percentage]%)
vs [comparison basis] for [period]

Driver: [Primary driver description]
[2-3 sentences explaining the business reason for the variance, with specific
quantification of contributing factors]

Outlook: [One-time / Expected to continue / Improving / Deteriorating]
Action: [None required / Monitor / Investigate further / Update forecast]

Narrative Quality Checklist

Good variance narratives should be:

  • Specific: Names the actual driver, not just "higher than expected"
  • Quantified: Includes dollar and percentage impact of each driver
  • Causal: Explains WHY it happened, not just WHAT happened
  • Forward-looking: States whether the variance is expected to continue
  • Actionable: Identifies any required follow-up or decision
  • Concise: 2-4 sentences, not a paragraph of filler

Common Narrative Anti-Patterns to Avoid

  • "Revenue was higher than budget due to higher revenue" (circular — no actual explanation)
  • "Expenses were elevated this period" (vague — which expenses? why?)
  • "Timing" without specifying what was early/late and when it will normalize
  • "One-time" without explaining what the item was
  • "Various small items" for a material variance (must decompose further)
  • Focusing only on the largest driver and ignoring offsetting items

Waterfall Chart Methodology

Concept

A waterfall (or bridge) chart shows how you get from one value to another through a series of positive and negative contributors. Used to visualize variance decomposition.

Data Structure

Starting value:  [Base/Budget/Prior period amount]
Drivers:         [List of contributing factors with signed amounts]
Ending value:    [Actual/Current period amount]

Verification:    Starting value + Sum of all drivers = Ending value

Text-Based Waterfall Format

When a charting tool is not available, present as a text waterfall:

WATERFALL: Revenue — Q4 Actual vs Q4 Budget

Q4 Budget Revenue                                    $10,000K
  |
  |--[+] Volume growth (new customers)               +$800K
  |--[+] Expansion revenue (existing customers)      +$400K
  |--[-] Price reductions / discounting               -$200K
  |--[-] Churn / contraction                          -$350K
  |--[+] FX tailwind                                  +$50K
  |--[-] Timing (deals slipped to Q1)                 -$150K
  |
Q4 Actual Revenue                                    $10,550K

Net Variance: +$550K (+5.5% favorable)

Bridge Reconciliation Table

Complement the waterfall with a reconciliation table:

DriverAmount% of VarianceCumulative
Volume growth+$800K145%+$800K
Expansion revenue+$400K73%+$1,200K
Price reductions-$200K-36%+$1,000K
Churn / contraction-$350K-64%+$650K
FX tailwind+$50K9%+$700K
Timing (deal slippage)-$150K-27%+$550K
Total variance+$550K100%

*Note: Percentages can exceed 100% for individual drivers when there are offsetting items.*

Waterfall Best Practices

  1. Order drivers from largest positive to largest negative (or in logical business sequence)
  2. Keep to 5-8 drivers maximum — aggregate smaller items into "Other"
  3. Verify the waterfall reconciles (start + drivers = end)
  4. Color-code: green for favorable, red for unfavorable (in visual charts)
  5. Label each bar with both the amount and a brief description
  6. Include a "Total Variance" summary bar

Budget vs Actual vs Forecast Comparisons

Three-Way Comparison Framework

MetricBudgetForecastActualBud Var ($)Bud Var (%)Fcast Var ($)Fcast Var (%)
Revenue$X$X$X$XX%$XX%
COGS$X$X$X$XX%$XX%
Gross Profit$X$X$X$XX%$XX%

When to Use Each Comparison

  • Actual vs Budget: Annual performance measurement, compensation decisions, board reporting. Budget is set at the beginning of the year and typically not changed.
  • Actual vs Forecast: Operational management, identifying emerging issues. Forecast is updated periodically (monthly or quarterly) to reflect current expectations.
  • Forecast vs Budget: Understanding how expectations have changed since planning. Useful for identifying planning accuracy issues.
  • Actual vs Prior Period: Trend analysis, sequential performance. Useful when budget is not meaningful (new business lines, post-acquisition).
  • Actual vs Prior Year: Year-over-year growth analysis, seasonality-adjusted comparison.

Forecast Accuracy Analysis

Track how accurate forecasts are over time to improve planning:

Forecast Accuracy = 1 - |Actual - Forecast| / |Actual|

MAPE (Mean Absolute Percentage Error) = Average of |Actual - Forecast| / |Actual| across periods
PeriodForecastActualVarianceAccuracy
Jan$X$X$X (X%)XX%
Feb$X$X$X (X%)XX%
...............
AvgMAPEXX%

Variance Trending

Track how variances evolve over the year to identify systematic bias:

  • Consistently favorable: Budget may be too conservative (sandbagging)
  • Consistently unfavorable: Budget may be too aggressive or execution issues
  • Growing unfavorable: Deteriorating performance or unrealistic targets
  • Shrinking variance: Forecast accuracy improving through the year (normal pattern)
  • Volatile: Unpredictable business or poor forecasting methodology

平台分布

Codex

36.32%
按下载量换算2,961

Claude

28.35%
按下载量换算2,311

Cursor

18.64%
按下载量换算1,520

Gemini CLI

8.79%
按下载量换算717

安全审计

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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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