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 guia-matthieu/clawfu-skills --skill revenue-attributiongit clone --depth 1 https://github.com/guia-matthieu/clawfu-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/guia-matthieu/clawfu-skills/revenue-attribution)<a href="https://agentmods.dev/skills/guia-matthieu/clawfu-skills/revenue-attribution"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/revenue-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/guia-matthieu/clawfu-skills/revenue-attribution"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/revenue-attribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00023 | $0.03287 |
| Opus 5 | $0.00012 | $0.01643 |
| Sonnet 5 | $0.00005 | $0.00657 |
| Haiku 4.5 | $0.00002 | $0.00329 |
Grade A, and why
revenue-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 9d 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Attribution
Determine which marketing and sales activities drive revenue using multi-touch attribution models, enabling smarter budget allocation and campaign optimization.
When to Use This Skill
- Justifying marketing spend to leadership
- Optimizing channel mix allocation
- Evaluating campaign ROI
- Resolving marketing/sales credit disputes
- Building attribution reports
Methodology Foundation
Based on Bizible/Marketo Multi-Touch Attribution and Google Analytics Attribution Models, covering:
- First-touch attribution (awareness credit)
- Last-touch attribution (conversion credit)
- Linear attribution (equal credit)
- Time-decay attribution (recency-weighted)
- Position-based (U-shaped, W-shaped)
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Explains attribution models | Which model fits your business |
| Calculates credit distribution | How to act on insights |
| Identifies top-performing channels | Budget reallocation amounts |
| Shows model comparison | Final attribution policy |
| Highlights discrepancies | Exception handling |
What This Skill Does
- Model education - Explain different attribution approaches
- Credit calculation - Apply models to touchpoint data
- Channel analysis - Compare performance by source
- Model comparison - Show how results differ by model
- Optimization recommendations - Where to invest more/less
How to Use
Analyze attribution for this closed-won deal:
Deal: [Company Name]
Value: $[Amount]
Close Date: [Date]
Sales Cycle: [Days]
Touchpoint Journey:
1. [Date] - [Channel] - [Action]
2. [Date] - [Channel] - [Action]
...
[List all touchpoints chronologically]
Questions:
- Which channels deserve credit?
- Compare first-touch vs last-touch
- Recommend budget allocation
Instructions
Step 1: Understand Attribution Models
| Model | Logic | Best For |
|---|---|---|
| First-Touch | 100% to first interaction | Awareness measurement |
| Last-Touch | 100% to final conversion | Direct response |
| Linear | Equal split across all | Long consideration cycles |
| Time-Decay | More credit to recent | Sales-assisted journeys |
| Position-Based | 40/20/40 (first/middle/last) | Balanced view |
| W-Shaped | 30/30/30 + 10 remainder | Include MQL moment |
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.
- 9d ago First seen · 422 lines · 23 tokens per session scan A 82ebf83b8fa3
revenue-attribution is a skill published in the GitHub repository guia-matthieu/clawfu-skills (149 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 3,287 once invoked, about $0.0001 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 2026-09-03.
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