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/wangke19/gemini-ai-helpersnpx agentmods add commands/wangke19/gemini-ai-helpers/bootstrap-omWrote 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/bootstrap-om)<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/bootstrap-om"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/bootstrap-om.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 | $0.00016 | $0.04108 |
| Opus 5 | $0.00008 | $0.02054 |
| Sonnet 5 | $0.00003 | $0.00822 |
| Haiku 4.5 | $0.00002 | $0.00411 |
Grade A, and why
bootstrap-om 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 — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
openshift:bootstrap-om
Synopsis
/openshift:bootstrap-om
Description
The openshift:bootstrap-om command automates the complete integration of OpenShift Manager (OM) into OpenShift operators. OM (formerly Multi-Operator Manager/MOM) is a framework designed to reduce duplicate effort and improve consistency across different OpenShift cluster topologies (standalone/OCP and Hypershift/HCP) by centralizing operator management and enabling comprehensive testing.
This command automates:
- Automatic resource discovery - Analyzes the codebase to identify all input and output resources
- Command implementation - Creates the three required OM commands (input-resources, output-resources, apply-configuration)
- Test infrastructure setup - Configures Makefile targets and test directories
- Test scenario creation - Generates initial test cases with proper structure
- Integration validation - Runs tests to ensure everything works correctly
Note: These instructions are optimized for operators built with github.com/openshift/library-go.
OpenShift Manager (OM) Overview
OM enables centralized operator management by requiring operators to declare their resource dependencies and configuration logic:
- input-resources - Lists all Kubernetes API resources the operator needs to read/watch
- output-resources - Maps Kubernetes API resources the operator creates/manages to cluster types (Configuration/Management/UserWorkload)
- apply-configuration - Runs operator logic in isolation using a manifestclient (a Kubernetes client that reads from a must-gather-like input directory instead of the API server): syncs once, outputs resulting resources to an output directory, then exits.
These declarations enable:
- Production Runtime: A single OperatorManager binary can communicate with the Kubernetes API server on behalf of multiple operators, maintaining shared caches and implementing rate limiting
- Testing: The
apply-configurationcommand validates operator behavior without a live cluster by using the manifestclient for file-based input/output - Consistency: Ensure or facilitate identical operator behavior across different cluster topologies (standalone, Hypershift/HCP)
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 · 464 lines · 16 tokens per session scan A 0a6311568fac
bootstrap-om is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 4,108 once invoked, about $0.0001 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.
Other commands, from other repositories
red-team
description: "Red team AI system for vulnerabilities".
integration-test-cycle
Self-iterating integration test workflow with codebase exploration, test development, autonomous test-fix cycles, and reflection-driven strategy adjustment.
bmad-review-verification-gap
Deprecated — forwards to bmad-review.
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.