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 dalroot/agentic-seo --skill conversiongit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/conversion)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/conversion"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/conversion/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/dalroot/agentic-seo/conversion"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/conversion.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 | $0.00076 | $0.01036 |
| Opus 5.5 | $0.00030 | $0.00414 |
| Sonnet 5.5 | $0.00015 | $0.00207 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
conversion-optimization 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 today.
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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategies: Conversion Optimization
Guides conversion rate optimization (CRO): increasing the percentage of visitors who complete desired actions. Higher conversion rates mean increased revenue, reduced CAC, and better ROI. Use this skill when optimizing funnels, running experiments, or reducing friction on high-traffic pages.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read Sections 4 (Audience), 5 (Website), 6 (Keywords).
Identify:
- Funnel stage: Awareness, consideration, decision, post-purchase
- Conversion goal: Signup, purchase, download, demo request
- Traffic: Volume; mobile vs desktop split
- Current conversion rate: Baseline for improvement
CRO Process
| Step | Action |
|---|---|
| 1. Research | Map funnel; identify high-traffic, low-conversion pages |
| 2. Hypothesize | Form testable hypothesis (if X, then Y because Z) |
| 3. Prioritize | Score by Potential, Importance, Ease (PIE) |
| 4. Test | A/B or multivariate; adequate sample size |
| 5. Analyze | Statistical significance; implement winner |
PIE Prioritization Framework
Score each test idea 1–10:
| Factor | Question |
|---|---|
| Potential | How much improvement is possible? |
| Importance | How much traffic does this page get? |
| Ease | How easy to implement? |
Rank backlog by total score; run highest-impact tests first.
A/B Testing Best Practices
| Practice | Guideline |
|---|---|
| Sample size | Calculate minimum before launch; 95% significance without adequate sample = false positives |
| Duration | Run full week cycles; account for day-of-week effects |
| One variable | Test one element per experiment (or use MVT for multiple) |
| Mobile separate | Mobile converts ~50% of desktop; test mobile independently—thumb reach, form complexity differ |
| Low traffic | Use Bayesian testing for faster, actionable results |
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.
- today First seen · 99 lines · 76 tokens per session scan A 03e9b85ef7d3
conversion-optimization is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 76 tokens to every session and 1,036 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 2026-10-26.
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