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-product --skill ai-feature-validationgit clone --depth 1 https://github.com/RBraga01/builder-productWrote 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-product/ai-feature-validation)<a href="https://agentmods.dev/skills/rbraga01/builder-product/ai-feature-validation"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-product/ai-feature-validation/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-product/ai-feature-validation"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-product/ai-feature-validation.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 | $0.00053 | $0.01857 |
| Opus 5 | $0.00026 | $0.00928 |
| Sonnet 5 | $0.00011 | $0.00371 |
| Haiku 4.5 | $0.00005 | $0.00186 |
Grade A, and why
ai-feature-validation 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 8d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feature Validation
The Law
AI FEATURES HAVE FAILURE MODES THAT DETERMINISTIC FEATURES DO NOT.
"We validated it like any other feature" misses hallucination UX, trust calibration, scope creep, and irreversibility — none of which appear in a standard acceptance criterion.
All four AI-specific risks addressed in the PRD IS AI feature validation.
When to Use
Trigger before:
- Approving any AI feature for development
- Adding an LLM call to any user-facing surface
- Expanding an existing AI feature's scope or capability
- Reviewing a PRD that includes AI-generated content, predictions, or decisions
When NOT to Use
- Backend AI pipelines with no user-visible output (use ai-safety-review from builder-ai instead)
- Features where the model output is filtered through deterministic rules before reaching users (the rules are the product; validate the rules)
The Four AI-Specific Risks
Standard PRD review asks: does the feature solve the user problem? AI feature validation asks four additional questions that standard PRDs never reach.
Risk 1 — Hallucination UX
What does the product look like when the model is wrong?
Every AI feature produces incorrect outputs. The question is not "will it hallucinate?" but "what does the UX do when it does?"
Required answers in the PRD:
- What is the worst plausible incorrect output for this feature? (e.g., "a medical dosage that is wrong by an order of magnitude")
- What does the user see when the model is wrong? Is it distinguishable from a correct output?
- Is there a confidence signal that alerts users to lower-confidence outputs?
- Is there a correction mechanism — can the user flag, override, or report an incorrect output?
- For high-stakes domains: is there an explicit disclaimer at the point of use?
BLOCK if:
- Incorrect outputs are visually identical to correct outputs with no uncertainty signal
- There is no correction mechanism for high-stakes outputs (medical, legal, financial, safety)
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.
- 8d ago First seen · 173 lines · 0 tokens per session scan A e2c55481bfc5
ai-feature-validation is a skill published in the GitHub repository RBraga01/builder-product (2 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 1,857 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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