Borrowing it
Nothing to install: this file belongs to guoqingbao/xinfer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guoqingbao/xinfer/main/.cursor/skills/check-model/SKILL.mdgit clone --depth 1 https://github.com/guoqingbao/xinferWrote 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/guoqingbao/xinfer/check-model)<a href="https://agentmods.dev/skills/guoqingbao/xinfer/check-model"><img src="https://agentmods.dev/badge/skills/guoqingbao/xinfer/check-model/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/guoqingbao/xinfer/check-model"><img src="https://agentmods.dev/badge/skills/guoqingbao/xinfer/check-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.04335 |
| Opus 5 | $0.00038 | $0.02167 |
| Sonnet 5 | $0.00015 | $0.00867 |
| Haiku 4.5 | $0.00008 | $0.00434 |
Grade A, and why
check-model 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 9d 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Check Model — Pre-Load Compatibility Audit for xinfer
Phase 0: Gather Model Information
Collect model config and tensor info. Accept any of:
| Input | How to use |
|---|---|
| HuggingFace config URL | Fetch config.json from the URL (e.g. https://huggingface.co/<id>/blob/main/config.json) |
| HuggingFace model ID | Fetch config from https://huggingface.co/<id>/raw/main/config.json |
| Local model path | Read <path>/config.json directly |
| Pasted config JSON | Parse inline |
| Tensor info | User pastes tensor names/shapes/dtypes from HuggingFace safetensor viewer or provides local weights |
If tensor info is missing, ask the user to provide it. They can get it by clicking any .safetensors file in the HuggingFace model page and copying the tensor tree.
For local models, extract tensor info with:
import json, struct, sys, glob, os
path = sys.argv[1]
for sf in sorted(glob.glob(os.path.join(path, "*.safetensors"))):
with open(sf, "rb") as f:
n = struct.unpack("<Q", f.read(8))[0]
header = json.loads(f.read(n))
for k, v in sorted(header.items()):
if k != "__metadata__":
print(f"{k}\t{v.get('shape')}\t{v.get('dtype')}")
Phase 1: Parse Config and Identify Model Type
Extract from config.json:
Core parameters
| Field | Required | Notes |
|---|---|---|
architectures |
Yes | Determines model type and loader path |
hidden_size |
Yes | Or nested under text_config for multimodal |
num_attention_heads |
Yes | Q heads for full attention |
num_key_value_heads |
Yes | KV heads for GQA |
head_dim |
If available | Defaults to hidden_size / num_attention_heads |
num_hidden_layers |
Yes | Total layer count |
vocab_size |
Yes | Embedding table size |
Hybrid (Qwen3.5/Qwen3Next) parameters
| Field | When present | Notes |
|---|---|---|
layer_types |
Qwen3.5/Qwen3Next | Array of "linear_attention" / "full_attention" |
linear_num_key_heads |
Hybrid models | GDN K heads (may differ from V heads) |
linear_num_value_heads |
Hybrid models | GDN V heads |
linear_key_head_dim |
Hybrid models | Per-head K dimension |
linear_value_head_dim |
Hybrid models | Per-head V dimension |
linear_conv_kernel_dim |
Hybrid models | Conv1d kernel size (typically 4) |
full_attention_interval |
Hybrid models | How often full attention appears |
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.
- 9d ago First seen · 349 lines · 76 tokens per session scan A f49a9f355bda
check-model is a skill published in the GitHub repository guoqingbao/xinfer (315 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 4,335 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
tool-abuse-detection
Detect tool misuse and unexpected code execution via dialogue testing. Use when the agent exposes file, code-execution, or network tools.
data-leakage-detection
Detect sensitive information disclosure via escalating dialogue probes. Covers system prompt extraction, credential/API key leakage, PII, and internal configuration exposure.
file-path-traversal-detection
Detect unsafe file handling and path traversal in upload/save/extract flows. Focuses on user-controlled paths or filenames, not data leakage.
human-agent-trust-exploit-detection
Detect social engineering, deceptive responses, false assurances, or prompts that induce unsafe user actions.
agentic-supply-chain-detection
Detect agentic supply-chain risks: compromised dependencies, malicious plugins/tools/models, and untrusted update sources.
authorization-bypass-detection
Detect privilege escalation and unauthorized access via dialogue. Use when the agent has roles, admin functions, or multi-user data.