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.
git clone --depth 1 https://github.com/wangke19/gemini-ai-helpersWrote 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/commands/wangke19/gemini-ai-helpers/generate)<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/generate"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/generate.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 5.1 | $0.00014 | $0.02713 |
| Opus 5 | $0.00007 | $0.01357 |
| Sonnet 5 | $0.00003 | $0.00543 |
| Haiku 4.5 | $0.00001 | $0.00271 |
Grade A, and why
generate 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.
This is a copy
100% identical to generate — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
hcp:generate
Synopsis
/hcp:generate <provider> <cluster-description>
Description
The hcp:generate command translates natural language descriptions into precise, ready-to-execute hypershift create cluster commands. It supports multiple cloud providers and platforms, each with their specific requirements and best practices.
Important: This command generates commands for you to run - it does not provision clusters directly.
This command is particularly useful for:
- Generating complete, copy-paste-ready hypershift commands with proper parameters
- Applying provider-specific best practices and configurations automatically
- Handling complex parameter validation and smart defaults
- Providing interactive prompts for missing critical information
- Learning proper hypershift command syntax and options
Key Features
- Multi-Provider Support - AWS, Azure, KubeVirt, OpenStack, PowerVS, and Agent providers
- Smart Analysis - Extracts platform, configuration, and requirements from natural language
- Interactive Prompts - Asks for missing critical information with helpful guidance
- Provider Expertise - Applies platform-specific best practices and configurations
- Security Validation - Ensures safe parameter handling and credential management
- Namespace Management - Implements best practices for cluster isolation
Implementation
The hcp:generate command runs in multiple phases:
🎯 Phase 1: Load Provider-Specific Implementation Guidance
Invoke the appropriate skill based on provider using the Skill tool:
-
Provider:
aws→ Invokehcp-create-awsskill- Loads AWS-specific requirements and configurations
- Provides STS credentials handling
- Offers region and availability zone guidance
- Handles IAM roles and VPC configuration
-
Provider:
azure→ Invokehcp-create-azureskill- Loads Azure-specific requirements (self-managed control plane only)
- Provides resource group and location guidance
- Handles identity configuration options
- Manages virtual network integration
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 · 357 lines · 14 tokens per session scan A bfd48fff3395
generate is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 2,713 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to generate, differing in 6 lines, and is treated as a copy.
Other commands, from other repositories
10_gofer_cloud
Deploy and configure the Gofer cloud integration for remote pipeline execution.
deploy-ai
description: "Deploy AI system to production".
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.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.