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Roi Calculator

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aaron-he-zhu
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, 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

Overview

Publisheraaron-he-zhu
Repositoryaaron-marketing-skills
Skill nameroi-calculator
Stars
2.8K
Forks
361
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aaron-he-zhu on GitHub. Read the source before you install it.

Installation

Install the Roi Calculator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git /tmp/aaron-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/aaron-marketing-skills/influencer/report/roi-calculator .claude/skills/roi-calculator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Roi Calculator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Roi Calculator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Roi Calculator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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, attribution-reconciler, 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

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

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 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 — 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)

  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)

  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)

  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)

  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)

  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)

  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)

  8. Generate the ROI report summary — investment, returns, ROI by methodology, key metrics vs. benchmark, bottom line, and 1-3 recommendations. (template)

  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 R1R6.

    Hand this Return evidence to the creator-content-auditor 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.

Reference Materials

Next Best Skill

Primary: report-generator — turn the ROI numbers into a stakeholder-ready report.

Alternates (same Report family):

  • performance-analyzer — go back for deeper performance breakdowns if the ROI math exposed gaps.
  • budget-optimizer — feed by-influencer and by-tier ROI into the next budget allocation.

Termination note: keep a visited-set of skills invoked this session. If the primary next skill was already run, stop and report the chain complete rather than re-invoking it. Stop after at most 3 hops in a single chain.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Roi Calculator AI skill do?

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

Why use Roi Calculator on TypingMind?

Because you install it once and use it with any model. Roi Calculator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Roi Calculator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/influencer/report/roi-calculator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Roi Calculator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Roi Calculator?

As many as you like. As long as a model supports skills, you can use Roi Calculator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Roi Calculator AI skill free?

Yes. It is published on GitHub by aaron-he-zhu under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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