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.
npx skills add LeadMagic/gtm-skills --skill attributiongit clone --depth 1 https://github.com/LeadMagic/gtm-skillsWrote 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.
[](https://agentmods.dev/skills/leadmagic/gtm-skills/attribution)<a href="https://agentmods.dev/skills/leadmagic/gtm-skills/attribution"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/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.
<a href="https://agentmods.dev/skills/leadmagic/gtm-skills/attribution"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/attribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.05702 |
| Opus 5.5 | $0.00026 | $0.02281 |
| Sonnet 5.5 | $0.00013 | $0.01140 |
| Haiku 4.5 | $0.00006 | $0.00570 |
Grade A, and why
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 23d 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.
How it starts
The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attribution
Overview
Attribution answers the existential marketing question: which of our activities actually produce revenue? The core principle is that single-touch attribution lies. First-touch attribution over-credits awareness channels. Last-touch attribution over-credits bottom-of-funnel conversion channels. Both lead to systematically bad investment decisions — over-funding channels that appear in the credited position and starving channels that create the conditions for conversion.
The non-obvious rule: the "best" attribution model depends on your GTM motion. Product-led growth companies should weight product-qualified signals higher. Sales-led companies should weight sales engagement higher. Channel-led companies should weight partner influence. There is no universal attribution model — only the model that matches how your customers actually buy.
This skill produces: a Multi-Touch Attribution Model Report comparing 4-6 attribution models on revenue credit allocation, channel ROI calculations with cost and revenue attribution, a UTM governance framework with taxonomy and enforcement rules, a source-of-truth reporting structure, and optimization recommendations for budget reallocation.
When to Use
- User says "attribution" or "attribution model" → activate this skill
- User asks "which channel generates the most revenue" → use this skill
- User says "marketing ROI" or "campaign ROI" → attribution is required
- User mentions "UTM tracking" or "UTM hygiene" or "UTM parameters" → use UTM governance module
- User asks "how do I measure multi-touch attribution" → implement a model
- User says "source of truth reporting" or "marketing and sales disagree on numbers" → use this skill
- Trigger phrases: attribution modeling, multi-touch, first-touch, last-touch, channel attribution, campaign ROI, marketing ROI, UTM parameters, tracking governance
Do NOT use for:
- Campaign-level analytics (open rates, reply rates, meeting rates) → use campaign-analytics
- Pipeline management and forecasting → use pipeline-management
- A/B testing methodology → use a-b-testing
- CRM data architecture → use crm-integration
- Real-time channel monitoring and alerting → use proactive-alerts
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 23d ago Changed 80785e0533ed
- 1mo ago Changed 7160138430b8
- 1mo ago First seen · 372 lines · 64 tokens per session scan A 4f0a3d5929e5
attribution is a skill published in the GitHub repository LeadMagic/gtm-skills (67 stars, last pushed 11d ago), licensed MIT. It adds 64 tokens to every session and 5,702 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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