About six months ago I shipped a customer-facing feature where the system prompt had a subtle ambiguity in the instruction hierarchy. Within two days, users found a natural-language path that caused the model to ignore the safety constraint entirely.

It wasn’t a jailbreak — just phrasing I hadn’t anticipated. The prompt looked fine. It passed code review. It failed in production.

That made me realize how little tooling exists between “write a prompt” and “ship it.”

We have linters for code. We have type checkers. We have static analysis.

For prompts, we mostly have vibes.

So I built CostGuardAI.

npm install -g @camj78/costguardai costguardai analyze my-prompt.txt

It analyzes prompts across a few structural risk dimensions: - jailbreak / prompt injection surface - instruction hierarchy ambiguity - under-constrained outputs (hallucination risk) - conflicting directives - token cost + context usage

It outputs a CostGuardAI Safety Score (0–100, higher = safer) and shows what’s driving the risk.

Example:

CostGuardAI Safety Score: 58 (Warning)

Top Risk Drivers: - instruction ambiguity - missing output constraints - unconstrained role scope

The scoring isn’t trying to predict every failure — it’s closer to static analysis: catching structural patterns that correlate with prompts breaking in production.

If you want to see output before installing: https://costguardai.io/report/demo https://costguardai.io/benchmarks

I’m a solo founder and this is still early, but it’s already caught real issues in my own prompts.

Curious what HN thinks — especially from people working on prompt evals or LLM safety tooling.

techcam

Recent submissions

Show HN: CLI to score AI prompts after a prod failure (opens in new tab)

Recent submissions

Show HN: CLI to score AI prompts after a prod failure (opens in new tab)