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 RBraga01/builder-growth --skill ai-messaging-reviewgit clone --depth 1 https://github.com/RBraga01/builder-growthWrote 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/rbraga01/builder-growth/ai-messaging-review)<a href="https://agentmods.dev/skills/rbraga01/builder-growth/ai-messaging-review"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-growth/ai-messaging-review/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/rbraga01/builder-growth/ai-messaging-review"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-growth/ai-messaging-review.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 | $0.00050 | $0.01799 |
| Opus 5 | $0.00025 | $0.00899 |
| Sonnet 5 | $0.00010 | $0.00360 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
ai-messaging-review 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 11d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Messaging Review
The Law
AN AI CAPABILITY CLAIM WITHOUT EVIDENCE IS A LIABILITY, NOT A DIFFERENTIATOR.
"10x productivity" without measurement is a promise users test on their first session and don't forgive when it fails.
Every quantified claim sourced + every capability claim scoped + every "AI" label defined IS reviewed messaging.
When to Use
Trigger before:
- Publishing any marketing copy that makes claims about an AI product's capabilities
- Launching any campaign that references AI performance, accuracy, or productivity impact
- Publishing any press release, case study, or sales collateral about an AI product
- Making any public quantified claim ("saves X hours", "Y% more accurate", "Z× faster")
When NOT to Use
- Internal technical documentation (accuracy matters but external credibility risk is lower)
- Product UI copy describing what a feature does — tested separately with
copy-quality-gate
The Four Review Categories
Every piece of AI marketing copy is reviewed against all four. One failure in any category is a fail.
Category 1 — Quantified Claims
Every number in AI marketing copy must have a source.
Required for any quantified claim:
- The study or measurement that produced the number
- The population it was measured on (user segment, task type, timeframe)
- The comparison baseline (X× faster than what? Y% more accurate than what?)
- Whether it is reproducible by a new customer
✗ "Save 10 hours per week"
— no measurement, no population, no baseline, not reproducible
✓ "In a study of 50 engineering teams using the tool for 90 days,
teams reported an average of 6.5 hours saved per engineer per week
compared to their previous process (survey, N=342, 95% CI: 5.8–7.2h)"
Simplification allowed: "Teams save an average of 6.5 hours per engineer per week (from our 2026 customer study — see details)." The full methodology must be available on request, not necessarily in the headline.
Rules:
- Self-reported surveys are evidence but must be labelled as surveys
- A single customer's result cannot be presented as a typical result
- "Up to X" claims must represent at least the 80th percentile, not the maximum
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.
- 11d ago First seen · 175 lines · 50 tokens per session scan A a8a158c0e584
ai-messaging-review is a skill published in the GitHub repository RBraga01/builder-growth (2 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 1,799 once invoked, about $0.0003 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 2026-08-31.
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