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 brainbytes-dev/everything-claude-marketing --skill analytics-attributiongit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWrote 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/brainbytes-dev/everything-claude-marketing/analytics-attribution)<a href="https://agentmods.dev/skills/brainbytes-dev/everything-claude-marketing/analytics-attribution"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/analytics-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/brainbytes-dev/everything-claude-marketing/analytics-attribution"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/analytics-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 | $0.00024 | $0.01972 |
| Opus 5.5 | $0.00010 | $0.00789 |
| Sonnet 5.5 | $0.00005 | $0.00394 |
| Haiku 4.5 | $0.00002 | $0.00197 |
Grade A, and why
analytics-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 2mo 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Attribution Modeling
When to Activate
- Allocating or reallocating marketing budget across channels
- Evaluating which campaigns or channels drive conversions
- Building or improving marketing measurement infrastructure
- Assessing impact of iOS/privacy changes on tracking
- Setting up UTM tracking and attribution tooling
- Debating "what's working" with stakeholders who disagree
- Running incrementality tests to validate attribution data
First Questions
- What is your current attribution model and tooling? (GA4, platform pixels, MTA vendor, MMM?)
- What does your conversion funnel look like? (Awareness -> consideration -> purchase -> retention)
- How long is your typical customer journey? (Same-day impulse vs. 90-day B2B sales cycle)
- What channels are you running? (Paid search, paid social, organic, email, direct, referral, affiliate)
- What is your primary conversion event? (Purchase, sign-up, demo request, app install)
- How much of your traffic is mobile vs. desktop? (Privacy impact assessment)
- Do you have a CRM or CDP connecting touchpoints to customers?
Core Attribution Models
Last-Click Attribution
- How it works: 100% credit to the final touchpoint before conversion.
- Best for: Direct-response campaigns, short purchase cycles, bottom-of-funnel optimization.
- Limitation: Ignores all awareness and consideration touchpoints. Massively over-credits branded search and retargeting.
- When to use: As a baseline only. Never as your sole model.
First-Click Attribution
- How it works: 100% credit to the first touchpoint in the journey.
- Best for: Understanding top-of-funnel channel effectiveness, awareness campaigns.
- Limitation: Ignores everything that happens after initial discovery.
- When to use: When evaluating demand generation and awareness investments.
Linear Attribution
- How it works: Equal credit distributed across all touchpoints.
- Best for: When you genuinely believe every touchpoint matters equally.
- Limitation: Treats a random display impression the same as a high-intent search click.
- When to use: Early-stage attribution when you lack data for more sophisticated models.
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.
- 2mo ago First seen · 174 lines · 24 tokens per session scan A a7ba7427d90f
analytics-attribution is a skill published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 24 tokens to every session and 1,972 once invoked, about $0.0001 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 2026-08-31.
analytics-attribution compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| analytics-attributionbrainbytes-dev/everything-claude-marketing | Skill | 10 | 7mo ago | MIT | A | 24 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |