omg-debug

A command called /omg-debug from the omg-payment-skill collection. Its description covers usage, AI execution steps, and Mac-specific parameters.

In plain words
What is it for?
Use it when you need to run or configure /omg-debug; more detail is needed to describe its concrete output.
Why use it?
It records how to invoke the debugging command and handle its Mac option, but the available description does not say what it debugs.

Command

Install

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.

agentmods
npx agentmods add commands/chenmitchell/omg-payment-skill/omg-debug
Clone the repo
git clone --depth 1 https://github.com/chenmitchell/omg-payment-skill
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 578 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00000 $0.00578
Opus 5 $0.00000 $0.00289
Sonnet 5 $0.00000 $0.00116
Haiku 4.5 $0.00000 $0.00058

Measured 2d ago against content hash abf223170992, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

omg-debug 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 2d 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.

commands/omg-debug.md · 53 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 2d ago First seen · 53 lines · 0 tokens per session scan A abf223170992

Subscribe to this mod's changes

omg-debug is a command published in the GitHub repository chenmitchell/omg-payment-skill (7 stars, last pushed 2mo ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 578 tokens. 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 2026-08-31.

Related

Other commands, from other repositories

jump

Help the user decide whether to stay at current job or take a new offer / look for new job. Analyzes value flow, irreplaceability trajectory, promise fulfillment history, opportunity cost, and real constraints.

workingclass-ai/workingclass · 43 tokens

law

Look up labor law in your jurisdiction. Provides framework for understanding worker rights, when to consult a lawyer, and authoritative information sources. Covers China, Hong Kong/Taiwan, US, Canada, Australia, UK, EU, India.

workingclass-ai/workingclass · 47 tokens

review

Decode a performance review or PIP to identify the real intent. Detects soft-PIP signals, vague-future-reward traps, scope-creep without compensation, and contributions being team-washed.

workingclass-ai/workingclass · 40 tokens

salary

Help user negotiate salary - whether for a new job offer, in-place raise, or recovering from years of underpayment. Identifies offer red flags and provides specific scripts.

workingclass-ai/workingclass · 34 tokens

triage

入口分诊。当用户不知道自己该用哪个模块、或情况复杂涉及多个模块时使用。最多3个问题判断该走哪条路径。Entry triage — routes the user to the right module(s) when they're unsure or facing a complex situation.

workingclass-ai/workingclass · 54 tokens

decode

Decode manipulative rhetoric in workplace emails/messages from boss, HR, or company communications. Identifies the real intent behind common phrases like "we are a family", "long-term vision", "be more proactive".

workingclass-ai/workingclass · 42 tokens