Show HN: CLI to score AI prompts after a prod failure(costguardai.io)
costguardai.io
Show HN: CLI to score AI prompts after a prod failure
https://costguardai.io
2 comments
Happy to explain how the scoring works since that’s the obvious first question.
The core idea is:
Safety Score = 100 − riskScore
The risk score is based on structural prompt properties that tend to correlate with failures in production systems:
- instruction hierarchy ambiguity - conflicting directives (system vs user) - missing output constraints - unconstrained response scope - token cost / context pressure
Each factor contributes a weighted amount to the total risk score.
It’s not trying to predict exact model behavior — that’s not possible statically.
The goal is closer to a linter: flagging prompt structures that are more likely to break (injection, hallucination drift, ignored constraints, etc).
There’s also a lightweight pattern registry. If a prompt matches structural patterns seen in real jailbreak/injection cases (e.g. authority ambiguity), the score increases.
One thing that surprised me while building it: instruction hierarchy ambiguity caused more real-world failures than obvious injection patterns.
The CLI runs locally — no prompts are sent anywhere.
If you want to try it:
npm install -g @camj78/costguardai costguardai analyze your-prompt.txt
Curious what failure modes others here have seen in production prompts.
The core idea is:
Safety Score = 100 − riskScore
The risk score is based on structural prompt properties that tend to correlate with failures in production systems:
- instruction hierarchy ambiguity - conflicting directives (system vs user) - missing output constraints - unconstrained response scope - token cost / context pressure
Each factor contributes a weighted amount to the total risk score.
It’s not trying to predict exact model behavior — that’s not possible statically.
The goal is closer to a linter: flagging prompt structures that are more likely to break (injection, hallucination drift, ignored constraints, etc).
There’s also a lightweight pattern registry. If a prompt matches structural patterns seen in real jailbreak/injection cases (e.g. authority ambiguity), the score increases.
One thing that surprised me while building it: instruction hierarchy ambiguity caused more real-world failures than obvious injection patterns.
The CLI runs locally — no prompts are sent anywhere.
If you want to try it:
npm install -g @camj78/costguardai costguardai analyze your-prompt.txt
Curious what failure modes others here have seen in production prompts.
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.