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 agentmods add commands/mitulgarg/env-doctor/modelgit clone --depth 1 https://github.com/mitulgarg/env-doctorWhat 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 | $0.00000 | $0.02232 |
| Opus 5 | $0.00000 | $0.01116 |
| Sonnet 5 | $0.00000 | $0.00446 |
| Haiku 4.5 | $0.00000 | $0.00223 |
Grade A, and why
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 2d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
model
Check if an AI model fits on your GPU before downloading.
Usage
env-doctor model <model-name>
Options
| Option | Description |
|---|---|
--list |
List all available models in local database |
--precision <type> |
Check specific precision (fp32, fp16, bf16, int8, int4, fp8) |
--recommend |
Show cloud GPU instance recommendations (AWS, GCP, Azure) sorted by cost |
--vram <MB> |
Direct VRAM requirement in MB (use with --recommend, without model name) |
Example
env-doctor model llama-3-8b
Output:
🤖 Checking: LLAMA-3-8B
Parameters: 8.0B
HuggingFace: meta-llama/Meta-Llama-3-8B
🖥️ Your Hardware:
RTX 3090 (24GB VRAM)
💾 VRAM Requirements & Compatibility
✅ FP16: 19.2GB (measured) - 4.8GB free
✅ INT4: 4.8GB (estimated) - 19.2GB free
✅ This model WILL FIT on your GPU!
💡 Recommendations:
1. Use fp16 for best quality on your GPU
Listing Available Models
env-doctor model --list
Output:
📋 Available Models
LLMs:
llama-3-8b, llama-3-70b, llama-3-405b
mistral-7b, mixtral-8x7b
qwen-7b, qwen-14b, qwen-72b
Diffusion:
stable-diffusion-1.5, stable-diffusion-xl, stable-diffusion-3
flux-schnell, flux-dev
Audio:
whisper-tiny, whisper-base, whisper-small
whisper-medium, whisper-large, whisper-large-v3
Language:
bert-base, bert-large
t5-small, t5-base, t5-large
Checking Specific Precision
env-doctor model stable-diffusion-xl --precision int4
Output:
🤖 Checking: STABLE-DIFFUSION-XL
Parameters: 6.6B
🖥️ Your Hardware:
RTX 3060 (12GB VRAM)
💾 INT4 Requirements:
~2.5GB VRAM (estimated)
✅ This model WILL FIT at INT4 precision!
Model Database
env-doctor includes a curated local database of 75+ popular models with measured VRAM usage, plus access to thousands of models via HuggingFace Hub API.
Supported Model Categories
The local database includes models across multiple categories:
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.
- 2d ago First seen · 288 lines · 0 tokens per session scan A d97f9840d924
model is a command published in the GitHub repository mitulgarg/env-doctor (172 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,232 tokens. 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 commands, from other repositories
add-language
新しい言語を piper-plus に追加するためのガイドです。引数 $ARGUMENTS に言語コード (例: de, it, da) を指定してください。.
pina-agentic
../../.opencode/commands/pina-agentic.md.
review-language
Command "review-language" from ayutaz/piper-plus, covering 新言語実装レビュー, 前提条件, レビュー手順, step 1: 10エージェント並列レビュー and step 2: レビュー結果集約.
initref
Build a reference for the implementation details of this project. Use provided summarize tool to get summary of the files. Avoid reading the content of many files yourself, as we might hit usage limits. Do read the content of important files though. Use the returned summaries to create reference files in /ref…
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.