Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mindspore-ai/akgnpx agentmods add skills/mindspore-ai/akg/akg-env-setupWrote 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/mindspore-ai/akg/akg-env-setup)<a href="https://agentmods.dev/skills/mindspore-ai/akg/akg-env-setup"><img src="https://agentmods.dev/badge/skills/mindspore-ai/akg/akg-env-setup/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/mindspore-ai/akg/akg-env-setup"><img src="https://agentmods.dev/badge/skills/mindspore-ai/akg/akg-env-setup.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 | $0.00060 | $0.03301 |
| Opus 5 | $0.00030 | $0.01650 |
| Sonnet 5 | $0.00012 | $0.00660 |
| Haiku 4.5 | $0.00006 | $0.00330 |
Grade A, and why
akg-env-setup 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
akg_agents 环境准备
⛔ 核心规则
- 检查失败后只有两条路径:询问用户是否有其他可用环境、询问用户是否同意自动安装。除此之外禁止任何操作。
- 遇到 🛑 必须调用
question工具并等待用户回复,不得跳过或自行决定。 - 禁止执行本文档未列出的命令。
流程总览
Step 0 解析 $HOME_DIR → 读取缓存
├─ 命中 ──────────────────────────────┬─ FULL_SETUP → Step 5
│ └─ 否则 → 结束
└─ 未命中 → Step 1 检查 akg_cli + LLM
├─ 通过 → Step 4 采集硬件/Framework/DSL
│ ├─ 4a 硬件
│ ├─ 4b Framework
│ ├─ 4c DSL & 后端推断
│ └─ 4d 写入缓存(必须,不可跳过)
│ ├─ FULL_SETUP → Step 5 → (Step 6 [可选,仅需要新依赖时]) → 结束
│ └─ 否则 → 结束
└─ 失败 → Step 2 询问用户(一次收集处理方式+环境信息)
├─ 已有环境 → Step 1(重试)
└─ 需要安装 → Step 3 → Step 1(重试)
Step 0: 解析 HOME_DIR & 读取环境缓存
首先执行以下命令获取 home 目录绝对路径,赋值给 $HOME_DIR:
echo $HOME
后续所有路径中的 $HOME_DIR 均使用此值。
然后读取 $HOME_DIR/.akg/check_env.md:
- 文件存在:
- FULL_SETUP → 跳到 Step 5(每次任务都需确认参数)
- 非 FULL_SETUP → 报告就绪,流程结束
- 文件不存在 → 进入 Step 1
Step 1: 执行两项检查
需要先确定 $ENV_TYPE、$CONDA_ENV/$VENV_PATH 和 $AKG_AGENTS_DIR。优先使用调用方提供的值,未提供时使用当前已激活的环境。
检查 1 — akg_cli 可用性:
conda:
conda run -n $CONDA_ENV --no-capture-output bash -c \
"cd $AKG_AGENTS_DIR && source env.sh && which akg_cli"
venv:
bash -c "source $VENV_PATH/bin/activate && cd $AKG_AGENTS_DIR && source env.sh && which akg_cli"
检查 2 — LLM 连通性:
conda:
conda run -n $CONDA_ENV --no-capture-output bash -c \
"cd $AKG_AGENTS_DIR && source env.sh && \
python tools/v2/use_llm_check/test_run_llm.py"
venv:
bash -c "source $VENV_PATH/bin/activate && cd $AKG_AGENTS_DIR && source env.sh && \
python tools/v2/use_llm_check/test_run_llm.py"
判定:
- ✅ 两项全部通过 → 进入 Step 4
- ❌ 任一失败 → 进入 Step 2,禁止自行尝试其他环境。
Step 2: 🛑 报告失败并询问用户
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 301 lines · 60 tokens per session scan A 1a4aa2baf3f2
akg-env-setup is a skill published in the GitHub repository mindspore-ai/akg (259 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 3,301 once invoked, about $0.0003 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 2026-09-03.
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