akg-env-setup

akg-env-setup is a skill for Claude Code, OpenCode from mindspore-ai/akg. It costs 60 tokens per session (3,301 once invoked), scanned A, original, Apache-2.0.

An environment setup procedure for AKG Agents that checks the local tools and language-model access, detects hardware and software details, and stores the results in a cache.

In plain words
What is it for?
It prepares and validates the environment for AKG operator-generation tasks, including hardware, framework, DSL, and backend detection.
Why use it?
It avoids repeating environment checks and gives the workflow the configuration it needs before running. If checks fail, it asks how to proceed instead of choosing an installation path on its own.

Skill for Claude CodeOpenCode

Written for Claude Code and OpenCode: argument-hint in frontmatter, but also installed under .opencode/.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python tools/v2/use_llm_check/test_run_llm.py".

Good fit It prepares and validates the environment for AKG operator-generation tasks, including hardware, framework, DSL, and backend detection.

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Install

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.

Clone the repo
git clone --depth 1 https://github.com/mindspore-ai/akg
agentmods
npx agentmods add skills/mindspore-ai/akg/akg-env-setup

Made for: Claude Code, OpenCode.

Wrote 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.

agentmods badge for akg-env-setup

README.md
[![agentmods](https://agentmods.dev/badge/skills/mindspore-ai/akg/akg-env-setup/github.svg)](https://agentmods.dev/skills/mindspore-ai/akg/akg-env-setup)
Your own site
<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.

agentmods 80×15 button for akg-env-setup

Your own site · 80×15
<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>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,301 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 1a4aa2baf3f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

akg_agents/workspace/.opencode/skills/akg-env-setup/SKILL.md · 301 lines

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

  1. 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。
  2. 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。
  3. 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/$VENV_PATH$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

Read the full file on GitHub · 301 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 301 lines · 60 tokens per session scan A 1a4aa2baf3f2

Subscribe to this mod's changes

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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