llm-verify

Verify an LLM API endpoint — model authenticity, billing inflation, relay provenance, performance and silent downgrades. Use when the user asks whether the model they are paying for is genuine, whether a relay or proxy is trustworthy, whether they are being overcharged, or whether a model has been quietly downgraded. 检测 LLM API 端点的真伪、计费掺水、中转来源、性能与降智；当用户问"我用的模型是不是真的"、"这个中转站靠谱吗"、"是不是被降智了"、"计费对不对"时使用。

llm-verify is a single-binary CLI that runs a black-box check against any LLM API endpoint and answers what the user actually wants to know: am I getting the model I am paying for, am I being overcharged, and how many relays sit on this path?

When to use this

Reach for this skill when the user asks anything like:

• "Is this relay/proxy trustworthy?" "Is this key really Claude?"
• "Has the model been downgraded?" "It feels dumber than it used to."
• "Is the billing right?" "The token counts look off."
• "Check this API endpoint for me."
• Debugging an endpoint that is slow, drops its stream, or breaks tool calls.

How to run it

bash

llm-verify --base-url <URL> --api-key <KEY> --model <MODEL_ID>

Flag	Meaning
--protocol anthropic|openai	Inferred from the URL and model name if omitted
--depth fast|balanced|forensic	Default balanced; forensic samples more — slower and costlier, but firmer
--claimed-model <ID>	Use when the vendor's advertised name differs from the ID you request
--lang en|zh	Report language; follows the system locale by default
-o report.html	HTML report path
--json report.json	Also emit machine-readable JSON
--no-open	Do not open a browser

Credentials can also come from .env or the environment: LLM_VERIFY_BASE_URL, LLM_VERIFY_API_KEY, LLM_VERIFY_MODEL.

Exit codes

Code	Meaning
0	Clean
1	Failing score, or a suspicious / counterfeit / inconclusive verdict
2	A hard gate tripped (silent fallback, shared-pool forwarding, tier downgrade, wrapper injection, cache replay, hidden prompt, response replay)

Suitable as a CI gate as-is.

Reading the result

The tool reports two independent axes. Do not conflate them.

• Authenticity: genuine / genuine-with-defects / relayed / suspicious / counterfeit / inconclusive
• Origin: direct from vendor / cloud platform / subscription-derived / relay / reconstructed channel / undetermined

A real model behind a relay is "relayed", not "counterfeit". That is a longer path, not a substituted model.

Reporting back to the user

1. Lead with the conclusion, then the evidence. They want to know whether they can rely on it, not all 40 probe lines.
2. Call out hard gates separately. They are facts no weighted score excuses.
3. Not tested is not passed. Say plainly which probes were skipped.
4. Carry the confidence across, especially for identity: adjacent versions inside one tier are genuinely hard to separate, and the tool abstains when the evidence is thin. Do not supply a verdict it declined to give.
5. Do not convict on the tool's behalf. One tier apart is within sampling noise; the tool does not accuse there, and neither should you.

Limits to state honestly

• Resolution stops at tier granularity (flagship / mid / light). Adjacent versions inside a tier cannot be separated without distribution baselines.
• The tier call depends on sampling; use --depth forensic when it matters.
• An injected system prompt contaminates identity fingerprints, which is why the contract layer runs first and downgrades identity confidence when it finds injection.
• It cannot prove the server-side weights are the official ones — only that behaviour does or does not match expectations.
• Quantised builds (int4 / fp8) can only be given a probability, never a verdict.