marketing-attribution

marketing-attribution is a skill for Claude Code from anhnguyen0905/codex-mcp. It costs 104 tokens per session (1,245 once invoked), scanned A, original, MIT.

A guide to marketing attribution, the methods businesses use to assign credit for conversions to advertising or other customer interactions. It covers rule-based models such as last-click and linear attribution, marketing mix modeling, and randomized holdout tests that measure whether spending caused results.

In plain words
What is it for?
Use it to choose attribution models, set attribution and lookback windows, distinguish click-through from view-through conversions, and plan incrementality or holdout tests.
Why use it?
It clarifies the difference between assigning credit and measuring what advertising actually caused. This helps avoid treating tracked customer journeys as proof that a channel created additional sales.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the codex-flow plugin — 65 skills, 2 commands, 1 MCP server shipped together

Good fit Use it to choose attribution models, set attribution and lookback windows, distinguish click-through from view-through conversions, and plan incrementality or holdout tests.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/anhnguyen0905/codex-mcp/marketing-attribution
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.

Any agent
npx skills add anhnguyen0905/codex-mcp --skill marketing-attribution
Clone the repo
git clone --depth 1 https://github.com/anhnguyen0905/codex-mcp

Made for: Claude Code.

Or install codex-flow, the plugin that ships this one along with the rest of its 65 skills, 2 commands, 1 MCP server.

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 marketing-attribution

README.md
[![agentmods](https://agentmods.dev/badge/skills/anhnguyen0905/codex-mcp/marketing-attribution/github.svg)](https://agentmods.dev/skills/anhnguyen0905/codex-mcp/marketing-attribution)
Your own site
<a href="https://agentmods.dev/skills/anhnguyen0905/codex-mcp/marketing-attribution"><img src="https://agentmods.dev/badge/skills/anhnguyen0905/codex-mcp/marketing-attribution/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for marketing-attribution

Your own site · 80×15
<a href="https://agentmods.dev/skills/anhnguyen0905/codex-mcp/marketing-attribution"><img src="https://agentmods.dev/badge/skills/anhnguyen0905/codex-mcp/marketing-attribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,245 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00104 $0.01245
Opus 5 $0.00052 $0.00622
Sonnet 5 $0.00021 $0.00249
Haiku 4.5 $0.00010 $0.00125

Measured 11d ago against content hash 9b7e0efe9bd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

marketing-attribution scanned grade A with 0 findings 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 11d ago.

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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/marketing-attribution/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Marketing Attribution (models, windows, incrementality)

Attribution answers "who gets credit", never "what did spend cause"

Every rule-based model is an accounting convention chosen before the data arrives: it redistributes credit among observed touchpoints and cannot say what would have happened without the spend. That question is causal and needs a holdout. Attribution is for budgeting hygiene and daily routing; incrementality decides whether a channel deserves funding at all.

The model menu and what each one is biased toward

last-click / last-touch  → over-credits closing, high-intent, retargeting, branded search
first-click / first-touch→ over-credits discovery, upper funnel, prospecting
linear                   → equal split; ignores that touches differ in influence
time-decay               → weights recency via a half-life; still last-click-leaning
data-driven / algorithmic→ fits observed paths (Shapley/Markov-style); sees only tracked touches
MMM                      → aggregate time-series on spend/seasonality/price/promo; no user IDs
incrementality / holdout → randomized exposed vs withheld; the only design measuring causation

Rule-based models differ only in the weighting rule, so "which is right" is unanswerable in their own terms — all are wrong about causation and differ in which channel they flatter. Data-driven models inherit every observability gap: untracked, offline and privacy-suppressed touches are absent from the path, so credit flows to whatever is measurable. MMM needs long history and real spend variance and reaches untrackable channels, but stays correlational until calibrated against experiments.

Windows, lookback, and view-through

State the window on every figure: click-through lookback (commonly 7 days), view-through lookback (commonly 1 day), and the post-install conversion window. Changing a window changes the reported result with no change in reality, so rule that out before believing a step-change. View-through conversions (impression, no click) are the softest currency here: a 1-day-view and a 7-day-click conversion are different objects, never summable, and view-through-heavy "performance" is unproven without a holdout.

Read the full file on GitHub · 90 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. 11d ago First seen · 90 lines · 104 tokens per session scan A 9b7e0efe9bd4

Subscribe to this mod's changes

marketing-attribution is a skill published in the GitHub repository anhnguyen0905/codex-mcp (3 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 1,245 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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