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 | $0.00000 | $0.00439 |
| Sonnet 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 12d 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.
- 12d 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 (145 stars, last pushed 7mo 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.
Other skills, from other repositories
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
debugging-and-error-recovery
Guides systematic root-cause debugging with hard rules against guess-fixes and symptom suppression. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Triggers on "this is broken", "tests are failing", "why doesn't this work", or any error output.
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
chaos-engineering
Guides systematic fault injection and resilience testing. Use when designing for high availability, verifying disaster recovery, testing failure modes, or building fault-tolerant systems. Use when you need to prove your system survives infrastructure failures, network partitions, dependency outages, or cascading…
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.