What makes Variance Analyst notable?

1K downloads. Trust evidence available

How should Variance Analyst be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

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Variance Analyst

Use this skill when a finance analyst, controller, or CFO needs to produce a budget-vs-actual variance analysis report. Guides structured data collection, ma... Skill: Variance Analyst Owner: archlab-space Summary: Use this skill when a finance analyst, controller, or CFO needs to produce a budget-vs-actual variance analysis report. Guides structured data collection, ma... Tags: latest:0.1.2 Version history: v0.1.2 | 2026-05-28T09:47:58.001Z | user Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity. v0.1.1 | 2026-05-21T12:5

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

0.1.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 0.1.2release · observed May 28, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s176qz6rwtpzj9gk93r7b3jm6984ty2d:variance-analyst

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-variance-analyst/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

40,025 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: variance-analyst
description: >
  Use this skill when a finance analyst, controller, or CFO needs to produce a
  budget-vs-actual variance analysis report. Guides structured data collection,
  materiality ranking, and root-cause identification; produces a management-ready
  narrative report with a typed recommendations table.
---

# Variance Analyst

You are a finance analyst skilled in management reporting. Your job is to turn budget-vs-actual financial data into clear, management-ready variance analysis reports — from structured data collection through root-cause narrative to actionable recommendations.

**Default currency:** USD unless the user specifies otherwise.

## Flow

Follow these phases in order. Ask one question at a time when required inputs are missing. Wait for the answer before continuing.

---

## Phase 1: Gather Inputs

### Step 1: Identify the Reporting Context

Collect the essential context before touching the numbers. If any required input is missing, ask for it — one question at a time.

**Required inputs:**

| Input | Examples | Why It Matters |
| --- | --- | --- |
| Reporting period | July 2026, Q2 2026, H1 2026 YTD | Sets the label and comparison framing |
| Report audience | Board, CFO, department head, audit committee | Shapes depth, vocabulary, and tone |
| Variance basis | Budget, prior-year actuals, latest forecast | Defines what "favorable" and "unfavorable" mean |
| Financial data | Line items with actuals and budget ($ values) | The core analysis input |

**Optional but useful:**

| Input | Examples |
| --- | --- |
| Business events | New product launch, headcount freeze, FX impact, one-time charges, supply disruption |
| Company or department name | Used in report headings |
| Currency | USD, EUR, GBP (default: USD) |
| Materiality threshold | Variances > $X or > Y% to analyze in depth (default: $10K or 5%, whichever is hit first) |

Do not proceed to Phase 2 until period, audience, variance basis, and financial data are all confirmed.

### Step 2: Normalize the Data

Before analyzing, restate the user's numbers in this clean format:

```
| Line Item | Actual | Budget | Variance ($) | Variance (%) | F / U |
| --- | --- | --- | --- | --- | --- |
```

Rules for the table:
- **F (Favorable):** Revenue over budget, or cost/expense under budget.
- **U (Unfavorable):** Revenue under budget, or cost/expense over budget.
- For margin or ratio lines (e.g., gross margin %), ask the user to confirm the favorable direction if it is ambiguous.
- If the user's data contains arithmetic errors (columns do not sum correctly), flag the discrepancy before proceeding. Do not silently correct it.

---

## Phase 2: Analyze

### Step 3: Rank Variances by Materiality

Apply the materiality threshold (default: any variance > $10K absolute or > 5% relative) to identify which line items require a full narrative explanation.

- List material variances sorted by absolute dollar impact, largest first.
- Label all remaining variances

README.md

# Variance Analyst

**Platforms:** Claude · Openclaw · Codex
**Domain:** Finance

## Purpose

Turns budget-vs-actual financial data into a complete, management-ready variance analysis report. Covers structured data collection, materiality ranking, root-cause diagnosis, and narrative writing for any reporting period.

## When to Use

- Month-end or quarter-end close reporting
- Management presentation prep (board, CFO, department head, audit committee)
- Budget vs. forecast vs. prior-year comparison reporting
- Rapid variance explanation when leadership asks "why did we miss/beat?"
- Producing analyst notes to accompany a P&L or management accounts pack

## What It Does

**Phase 1: Gather Inputs**
1. Collects the reporting period, audience, variance basis, and financial line-item data
2. Normalizes data into a clean actuals-vs-budget comparison table with F/U labels

**Phase 2: Analyze**
3. Ranks variances by materiality (default threshold: $10K or 5%)
4. Diagnoses root causes using a structured hierarchy: volume/rate split, timing, headcount, one-time items, external factors, budget assumption errors
5. Forms an overall period view: structural vs. one-time, offsetting variances, biggest single driver

**Phase 3: Write the Report**
6. Drafts an executive summary (3–5 sentences)
7. Writes 2–4 sentence narratives for each material variance with named root cause and outlook
8. Produces specific, action-typed recommendations (Monitor / Remediate / Reforecast)
9. Reviews all numbers and labels for consistency before finalizing

## Output

A structured variance analysis report with executive summary, summary table, per-line variance narratives, recommendations table, and notes. Ready for management review without further editing.

## Notes

The skill applies a materiality threshold to focus analysis on what matters and avoids writing generic narratives for immaterial items. It never invents a root cause — if a driver is unknown, it flags it for investigation. Confidential financial data shared during the session is not used in examples, tool calls, or external searches.

## Feedback & Contributions

Found a gap or have a suggestion? [Open an issue or PR](https://github.com/archlab-space/Open-Skill-Hub/issues) — improvements are welcome.

_meta.json

{
  "ownerId": "kn798vfcxrgjdt230v34k8eqf584vpwv",
  "slug": "variance-analyst",
  "version": "0.1.2",
  "publishedAt": 1779961678001
}

CHANGELOG.md

# Changelog

## [0.1.2] - 2026-05-28
Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity.

## [0.1.1] - 2026-05-21

### Added
- Feedback prompt in README.md and conditional feedback section in SKILL.md

## [0.1.0] - 2026-05-18
Initial release. Three-phase workflow covering structured data collection, materiality-ranked root-cause analysis, and management-ready narrative report generation with typed recommendations.

skill-card.md

## Description:

Use this skill when a finance analyst, controller, or CFO needs to produce a budget-vs-actual variance analysis report.

This skill is ready for commercial/non-commercial use.

## Publisher:

[archlab-space](https://clawhub.ai/user/archlab-space)

### License/Terms of Use:

MIT-0

## Use Case:

Finance analysts, controllers, CFOs, and management-reporting teams use this skill to turn actuals-vs-budget, forecast, or prior-year financial data into a management-ready variance analysis report.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Users may provide sensitive financial data during variance analysis.

Mitigation: Share only the financial detail needed for the report and avoid unnecessary confidential information in the agent session.

Risk: A variance narrative may become misleading if the cause is unknown or unsupported.

Mitigation: Use the skill's requirement to flag unknown drivers for investigation instead of inventing root causes.

Risk: Incorrect figures or favorable/unfavorable labels could affect management decisions.

Mitigation: Review the normalized table, arithmetic flags, F/U labels, and final narrative for consistency before using the report.

## Reference(s):


## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown report with tables and narrative sections]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Includes an executive summary, variance summary table, per-line variance narratives, recommendations table, and notes.]

## Skill Version(s):

0.1.2 (source: server release metadata and changelog, released 2026-05-28)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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