Skip to content
Agentmart
Submit a tool

roi-calculator

Use when the user asks to "calculate influencer ROI", "prove campaign value", or "what was our ROAS"; produces direct ROI/ROAS, earned media value…

Skill by aaron-he-zhu

IndexedApache-2.0★ 3,012
Write a review

Chalk Labs

Need help deploying roi-calculator?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/aaron-he-zhu-roi-calculator

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Use when the user asks to "calculate influencer ROI", "prove campaign value", or "what was our ROAS"; produces direct ROI/ROAS, earned media value, attribution-modeled revenue, LTV-based ROI, and a stakeholder-ready summary. Not for building the full slide/written report — use report-generator. 达人营销ROI计算/投资回报测算

Use cases: documents, presentations, marketing

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/roi-calculator

What's inside

Skill name
roi-calculator
Description
Use when the user asks to "calculate influencer ROI", "prove campaign value", or "what was our ROAS"; produces direct ROI/ROAS, earned media value, attribution-modeled revenue, LTV-based ROI, and a stakeholder-ready summary. Not for building the full slide/written report — use report-generator. 达人营销ROI计算/投资回报测算
License
Apache-2.0
Compatibility
Claude Code and compatible agent-skill hosts
Metadata
phase: report · author: aaron-he-zhu · version: 20.1.0 · discipline: influencer · geo-relevance: low
Instructions
145 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • roi-templates.md · references/

SKILL.md (first lines)

# ROI Calculator

This skill helps you calculate and communicate the return on investment for influencer marketing campaigns using various methodologies appropriate for your goals and available data.

> **Cross-discipline (paid ads):** this is the shared **return-math engine** for paid ads — [paid-measurement-loop](../../../ad/scale/paid-measurement-loop/SKILL.md), [attribution-reconciler](../../../ad/scale/attribution-reconciler/SKILL.md), and budget-optimizer delegate ROAS/CPA/payback ratios here rather than recomputing them. Save paid runs under `memory/ad/roi-calculator/`.

## Quick Start

Shortest invocation:

```
Calculate ROI for our influencer campaign: $25K spend, $72K revenue, 2.1M reach
```

Common scenario — compare methods before reporting:

```
What's the ROI of our campaign using direct revenue, EMV, and LTV-based methods?
```

## Skill Contract

- **Reads**: campaign ID, complete opaque creator scope when creator-level math is requested, spend/cost-basis breakdown, deduplicated results data (reach, impressions, engagements, clicks, conversions, attributed revenue, new customers), the predeclared attribution model/window/decision rule, AOV and economic LTV inputs if LTV is in scope, and prior performance output from `performance-analyzer`. Reuse authorized opaque `creator_ref` values; raw handles/names/URLs/provider IDs stay transient and never identify a saved row.
- **Writes**: return the ROI calculation and summary inline by default; save them to `memory/influencer/roi-calculator/YYYY-MM-DD-<topic>.md` (or the declared paid path) only with exact WARM-save authorization. Saved output uses `campaign_id`, complete opaque `creator_ref` scope, and opaque evidence/artifact refs—never raw identity locators.
- **Promotes**: only with separate exact authorization, promote durable headline numbers with their attribution window, source, and uncertainty to `memory/hot-cache.md`; a calculation or WARM-save request does not authorize this operation.
- **Done when**:
  1. At least one ROI methodology is computed with the inputs and formula shown.
  2. Each headline metric is stated against a declared, source-dated comparison target; no universal benchmark is invented.
  3. A bottom-line arithmetic assessment and 1-3 recommendations are written. Call the campaign profitable only when attributed revenue and a complete campaign cost basis are verified; otherwise state that profitability is unverified.
  4. Every denominator is verified numeric and `> 0`; zero, negative, missing, or incompatible denominators yield `undefined`/`NEEDS_INPUT`, never a ratio.
- **Primary next skill**: [report-generator](../report-generator/SKILL.md)

### Handoff Summary

> Emit the standard shape from [skill-contract.md §Handoff Summary Format](../../../references/skill-contract.md).

## Data Sources

This family is Tier 1 — it works with no live integrations. Ask the user for spend and results data and compute everything from those inputs. Connectors below can pull the numbers automatically when available:

- `~~social platform analytics` — reach, impressions, engagements, video views per platform for EMV and cost-per-metric math.
- `~~ecommerce / analytics` — revenue, conversions, link clicks, and AOV for direct ROI and attribution.
- `~~CRM` — new-customer counts, repeat-purchase rate, and lifetime value for LTV-based ROI.
- `~~influencer database` — per-influencer fees and tier data for by-influencer ROI.

With zero integrations, supply the investment and results tables by hand and the skill still produces every calculation. See [CONNECTORS.md](../../../CONNECTORS.md) for the free/keyless recipe per category.

## Instructions

When a user requests ROI calculation, work the steps below. Each step has a fill-in template in [references/roi-templates.md](references/roi-templates.md) — link the step number to its block there.

1. **Gather ROI inputs** — campaign details, the investment (total spend) table, and the results-data table. ([template](references/roi-templates.md#step-1--roi-calculation-inputs))

2. **Calculate direct ROI** — Simple ROI = (Revenue − Investment) / Investment × 100; ROAS = Revenue / Investment. Call the difference **net return under the declared formula**, not profit, unless the revenue attribution and complete cost basis are verified. State positive/zero/negative arithmetic return and a separate profitability-verification status. ([template](references/roi-templates.md#step-2--direct-roi-calculation))

3. **Calculate Earned Media Value (EMV)** — impression-based (Impressions × comparable CPM / 1000) or engagement-based (Engagements × comparable CPE) only from supplied or cited, source-dated comparators. Report applicable methods separately and use a predeclared selection/weighting rule; never average or add overlapping methods by default. If no valid comparator exists, return `NEEDS_INPUT` for EMV. ([template](references/roi-templates.md#step-3--earned-media-value-emv))

4. **Calculate cost-efficiency metrics** — CPM, CPR, CPE, CPV, CPC, CPA, and CAC. Compare only against a declared, source-dated target with a compatible market, window, and attribution basis; otherwise report the metric descriptively and mark the comparison pending. ([template](references/roi-templates.md#step-4--cost-efficiency-analysis))

5. **Apply attribution modeling** — use one predeclared model only after the complete conversion universe, order/event dedupe key, journey touchpoints, eligible channels, and allocation rule are supplied. Optional alternative models are visibly non-additive sensitivity scenarios over the same deduplicated conversion set; never select the most favorable model after seeing results. Missing inputs or model-selection authority returns `NEEDS_INPUT`, not attributed revenue. ([template](references/roi-templates.md#step-5--attribution-analysis))

6. **Calculate customer lifetime value impact** — label the result **LTV-Based ROI** only when `New Customers` is deduplicated against the controlling attribution universe and the supplied LTV is a complete contribution-margin basis with cohort, horizon, retention/churn, refunds, margin, discount/timing, first-order inclusion, source/date, and a positive compatible investment denominator. Then use `((New Customers × contribution-margin LTV) − Investment) / Investment × 100`. A revenue-LTV input produces only an **Estimated revenue-basis scenario** with its basis/horizon/status; it is not economic ROI or profit and is never added to direct attributed revenue or another LTV horizon. Missing fields return `NEEDS_INPUT`. ([template](references/roi-templates.md#step-6--lifetime-value-analysis))

7. **Calculate by-creator ROI** — only for a complete locked creator scope with resolved opaque refs, compatible windows/cost bases, and deduplicated attributed revenue. Per-creator and tier aggregate ROAS is `sum(attributed revenue) / sum(spend)`, never a simple mean; rank only under a predeclared rule. ([template](references/roi-templates.md#step-7--influencer-level-roi))

8. **Generate the ROI report summary** — investment, returns, ROI by methodology, key metrics vs. benchmark, bottom line, and 1-3 recommendations. ([template](references/roi-templates.md#step-8--roi-summary-report))

9. **Qualify candidate Return (R) evidence for the gate**

   The financial outputs from steps 1–8 are **candidate Return (R) evidence** for STAR: ROI/ROAS read against the declared target (`R1`) and the alternative-channel baseline (`R3`), CPE/CPM/CPA benchmarked on a normalized window (`R2`), KPI attainment versus the pre-registered target (`R4`), conversions attributed with a stated method and rigor (`R5`), and incremental impact separated from baseline where measurable (`R6`). A field is Measured only when its exact source, entity, observation window, and attribution basis are verified; arithmetic on User-provided or Estimated inputs is Calculated, not Measured. Return evidence applies only at `assessment_time: actual`; a forecast read has no `R1`–`R6`.

   Hand this Return evidence to the [creator-content-auditor](../../activate/creator-content-auditor/SKILL.md) gate — it folds R into the full actual STAR run and computes the profile-weighted **SQS**. This skill does not run the scorer or emit the composite. Unverified conversions emit `results-unverified`: report `R1`/`R2`/`R5` as low-confidence and make no attributable-return claims. These financial numbers are consumed as R evidence; they are not themselves an SQS.

   For a multi-creator campaign, the gate scores each creator partnership separately; a budget-weighted mean of the per-partnership SQS values may summarize the campaign but never replaces the per-partnership diagnosis. This skill supplies the per-partnership Return evidence; it does not aggregate or roll up a composite.

10. **Persist only with permission** — save under `memory/influencer/roi-calculator/` (or the paid path) only after authorization; request separate authorization for hot-cache promotion.

For every formula in this skill, verify the denominator is numeric and strictly greater than zero. If investment, impressions, reach, engagements, views, clicks, acquisitions, customers, or another required denominator is zero, negative, missing, or incompatible with the numerator window, report the ratio as `undefined` and return `NEEDS_INPUT` for that metric. Never silently divide by zero, coerce it, or substitute a nominal value.

## Example

**User**: "Calculate ROI for our influencer campaign: $25K spend, $72K revenue, 2.1M reach"

**Output**:

```markdown
# ROI Calculation Summary

## Investment & Returns

| Item | Value |
|------|-------|
| Total Investment | $25,000 |
| Revenue used in calculation | $72,000 (User-provided; source/window unverified) |
| Total Reach | 2,100,000 |

## ROI Results

### Direct ROI — calculated on User-provided revenue basis
- **Net return under the declared formula**: $47,000
- **ROI**: 188%
- **ROAS**: 2.88:1

The supplied revenue implies $2.88 per $1 spent. `results-unverified`: no attribution source, method, or window was supplied, so this is not an attributable, causal, or incremental-return claim.

### Earned Media Value
- **EMV**: `NEEDS_INPUT` — no source-dated comparable CPM/CPE or declared valuation rule was supplied
- **EMV Multiple**: `NEEDS_INPUT`

### Cost Efficiency
- **CPM**: $11.90
- **CPA**: Unknown (conversion count was not supplied)

## Assessment: Positive arithmetic return on the supplied basis; profitability and causality unverified

Supplied revenue exceeds supplied investment under the declared formula, but no complete cost basis, attribution source/window, source-dated peer target, or incrementality evidence was provided. Do not infer profit, benchmark outperformance, or causal lift, and do not authorize a scale decision from this read alone; obtain verified conversions, attribution evidence, complete costs, and the campaign owner's precommitted decision rule first.
```

The source-dated benchmark evidence template lives in [references/roi-templates.md#benchmark-evidence-template](references/roi-templates.md#benchmark-evidence-template).

From aaron-he-zhu/aaron-marketing-skills, Apache-2.0.

More from aaron-he-zhu/aaron-marketing-skills

Signals

Stars
3,012
Forks
386
Open issues
1
Last commit (this folder)
Sep 1, 2026 (2 months ago)
Last release
Sep 2, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
20.1.0
License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from aaron-he-zhu

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get roi-calculator running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying roi-calculator?

CallGet help
Cite this page

roi-calculator skill. Agent Mart. https://agentmart.dev/skills/aaron-he-zhu-roi-calculator (last updated 2026-10-24)