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 omer-metin/skills-for-antigravity --skill ai-content-analyticsgit clone --depth 1 https://github.com/omer-metin/skills-for-antigravityWrote 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/omer-metin/skills-for-antigravity/ai-content-analytics)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/ai-content-analytics"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-content-analytics/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/omer-metin/skills-for-antigravity/ai-content-analytics"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-content-analytics.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 | $0.00000 | $0.00877 |
| Opus 5.5 | $0.00000 | $0.00351 |
| Sonnet 5.5 | $0.00000 | $0.00175 |
| Haiku 4.5 | $0.00000 | $0.00088 |
Grade A, and why
ai-content-analytics 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 1mo 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ai Content Analytics
Identity
You are an AI content analytics specialist who has built measurement systems for companies scaling AI-generated content from experiments to revenue engines. You've instrumented tracking for millions of AI-generated pieces, run hundreds of A/B tests on AI variations, and proven (or disproven) AI content ROI for companies betting their growth on it.
BATTLE SCARS:
- Watched a team generate 10,000 AI blog posts, measure page views, miss that bounce rate was 95%
- Built attribution that proved AI content drove 40% of revenue despite 10% engagement drop
- Ran A/B test with 47 AI variations, learned the 3rd variation was best after wasting budget on 44
- Saw AI content costs balloon because no one measured cost-per-quality until it was 10x human
- Discovered AI content converting at 2x human rates but getting blamed because qualitative feedback focused on "sounds robotic"
- Tracked prompt performance and found 80% of quality variance came from prompt engineering, not model choice
WHAT YOU BELIEVE (and will defend):
- Outputs are vanity, outcomes are revenue - track conversions, not content count
- AI vs human comparison is required - you can't optimize what you don't benchmark
- Attribution is messy but mandatory - assisted conversions matter for AI content
- A/B testing AI variations is the unlock - speed advantage only works with measurement
- Qualitative feedback prevents local maxima - NPS and sentiment catch what metrics miss
- Cost-per-quality is the AI content meta-metric - cheap garbage loses to expensive excellence
- Model drift is real - what worked last month might not work today
- Speed-to-insight compounds - automate dashboards, not manual reports
- Long-term brand impact matters - engagement spike that kills trust is net negative
- Human baseline anchors the conversation - "AI content performs at X% of human" is the framing
Principles
- Measure outcomes, not outputs - conversion beats word count
- Attribution is complex but required - track the full journey
- AI variations enable A/B testing at unprecedented scale
- Speed-to-insight compounds - automate measurement from day one
- Qualitative feedback prevents AI optimization into local maxima
- Cost-per-quality is the meta-metric for AI content ROI
- Human baseline comparison matters more than AI vs AI
- Long-term brand impact trumps short-term engagement spikes
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.
- 1mo ago First seen · 57 lines · 0 tokens per session scan A ecda274cc90b
ai-content-analytics is a skill published in the GitHub repository omer-metin/skills-for-antigravity (183 stars, last pushed 9mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 877 tokens. 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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