variance-analysis

variance-analysis is a skill for Claude Code, Codex from miptah21/skills. It costs 43 tokens per session (2,620 once invoked), scanned C, a copy of variance-analysis, MIT.

A method for explaining why financial results differ from a budget or an earlier period. It separates changes into drivers such as price, volume, and product mix.

In plain words
What is it for?
Use it for budget-versus-actual reviews, period comparisons, revenue or cost analysis, and waterfall charts.
Why use it?
It turns a single unexplained variance into a narrative that shows what caused the change.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/miptah21/skills/variance-analysis
Any agent
npx skills add miptah21/skills --skill variance-analysis
Clone the repo
git clone --depth 1 https://github.com/miptah21/skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for variance-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/miptah21/skills/variance-analysis.svg)](https://agentmods.dev/skills/miptah21/skills/variance-analysis)
Your own site
<a href="https://agentmods.dev/skills/miptah21/skills/variance-analysis"><img src="https://agentmods.dev/badge/skills/miptah21/skills/variance-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,620 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin 98% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.02620
Opus 5 $0.00022 $0.01310
Sonnet 5 $0.00009 $0.00524
Haiku 4.5 $0.00004 $0.00262

Measured yesterday against content hash 079206a24126, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

variance-analysis scanned grade C with 1 finding 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 yesterday.

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.

Hidden instructionshighPrompt injection

Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.

<!-- lint-ignore-file-size -->
Origin

This is a copy

98% identical to variance-analysis — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/variance-analysis/SKILL.md · 268 lines

How it starts

The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 268 lines

Changes

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.

  1. yesterday First seen · 268 lines · 43 tokens per session scan C 079206a24126

Subscribe to this mod's changes

variance-analysis is a skill published in the GitHub repository miptah21/skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 2,620 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). It is 98% identical to variance-analysis, differing in 1 line, and is treated as a copy.

Related

Other skills, from other repositories

sector-rotation

行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架.

HKUDS/Vibe-Trading · 39 tokens

twitter-reader

Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…

himself65/finance-skills · 161 tokens

strategy-pivot-designer

Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

tradermonty/claude-trading-skills · 28 tokens

chenhao-limit-up

Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.

questflowai/investorskills · 44 tokens

trading-risk-gate

Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.

winstonkoh87/Athena-Public · 53 tokens

multi-expert-analyzer

针对通用问题进行多领域专家联合分析, 综合稿产生前必经 fact-checker 与 red-team 两道独立校验。适用场景: 用户提出跨领域或不确定领域的复杂问题, 需要从多个专家角度分别搜证并相互校验后综合成文, 例如该不该买房、该不该跳槽、是否进入某个赛道等。触发关键词: 多角度分析、专家分析、综合分析、多视角、跨领域分析、从不同角度看、深度分析。问题只属于单一明确领域时, 优先使用该领域的专门 skill, 例如纯财务用 finance-core-analysis、纯技术用 software-architect。输出 (全部 markdown 保存到当前项目 markdown/ 目录): (1) 每位专家的中间分析稿…

digoal/blog · 292 tokens