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 skills/inclusionai/aenvironment/aenvironment-deploynpx skills add inclusionAI/AEnvironment --skill aenvironment-deploygit clone --depth 1 https://github.com/inclusionAI/AEnvironmentWrote 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/inclusionai/aenvironment/aenvironment-deploy)<a href="https://agentmods.dev/skills/inclusionai/aenvironment/aenvironment-deploy"><img src="https://agentmods.dev/badge/skills/inclusionai/aenvironment/aenvironment-deploy.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00089 | $0.02251 |
| Opus 5 | $0.00044 | $0.01125 |
| Sonnet 5 | $0.00018 | $0.00450 |
| Haiku 4.5 | $0.00009 | $0.00225 |
Grade A, and why
aenvironment-deploy 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 4d 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 — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEnvironment Deploy
Automate deployment of sandboxed environment instances and services using the AEnvironment platform.
Overview
AEnvironment provides isolated sandbox environments for running agents and applications. This skill handles the complete deployment workflow:
- Instances: Short-lived environments for agents (IP-based access, no persistence)
- Services: Long-running services for apps (domain access, optional storage, multiple replicas)
Prerequisites
Install AEnvironment CLI:
pip install aenvironment --upgrade
aenv --help
Deployment Workflows
Workflow A: Deploy with Local Image Build
Build Docker image locally, register to EnvHub, and deploy.
When to use: Creating a new environment from scratch with local Dockerfile.
Requirements: Docker installed, registry credentials configured.
Script: scripts/deploy_with_local_build.py
Example:
python scripts/deploy_with_local_build.py \
--env-name myagent \
--owner-name john \
--api-service-url https://api.example.com \
--envhub-url https://envhub.example.com \
--registry-host registry.example.com \
--registry-username user \
--registry-password pass \
--registry-namespace myteam \
--deploy-type instance \
--ttl 24h \
--env-vars '{"API_KEY":"xxx"}'
Workflow B: Deploy with Existing Image
Register existing Docker image to EnvHub and deploy.
When to use: You have a pre-built Docker image to deploy.
Requirements: Existing image accessible in registry.
Script: scripts/deploy_with_existing_image.py
Example:
python scripts/deploy_with_existing_image.py \
--env-name myagent \
--image-name registry.example.com/myteam/agent:1.0.0 \
--owner-name john \
--api-service-url https://api.example.com \
--envhub-url https://envhub.example.com \
--deploy-type instance \
--ttl 48h
Workflow C: Deploy Existing Environment
Deploy from already registered environment in EnvHub.
When to use: Environment is already registered, just need to deploy.
What ships with it
7 files 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.
- references/CLI_COMMANDS.md 2.4 KB
- references/CONFIG_SCHEMA.md 2.8 KB
- references/TROUBLESHOOTING.md 5.3 KB
- scripts/aenv_operations.py 15 KB runs code
- scripts/deploy_existing_env.py 4.2 KB runs code
- scripts/deploy_with_existing_image.py 5.0 KB runs code
- scripts/deploy_with_local_build.py 5.9 KB runs code
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.
- 4d ago First seen · 346 lines · 89 tokens per session scan A 06803c991a76
aenvironment-deploy is a skill published in the GitHub repository inclusionAI/AEnvironment (314 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 2,251 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.
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