oh-my-opencode-slim is a plugin that coordinates multiple specialized AI agents inside OpenCode, assigning codebase exploration, documentation research, architecture review, design, and implementation to different agents. It is for developers who want agent teams to divide and reconcile software-development work while mixing models from different providers. The catalogue entries are the plugin’s bundled agents, skills, and instruction.
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 skills add alvinunreal/oh-my-opencode-slim --skill step-backgit clone --depth 1 https://github.com/alvinunreal/oh-my-opencode-slimWrote 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/alvinunreal/oh-my-opencode-slim/step-back)<a href="https://agentmods.dev/skills/alvinunreal/oh-my-opencode-slim/step-back"><img src="https://agentmods.dev/badge/skills/alvinunreal/oh-my-opencode-slim/step-back/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.
<a href="https://agentmods.dev/skills/alvinunreal/oh-my-opencode-slim/step-back"><img src="https://agentmods.dev/badge/skills/alvinunreal/oh-my-opencode-slim/step-back.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00212 |
| Opus 5.5 | $0.00000 | $0.00085 |
| Sonnet 5.5 | $0.00000 | $0.00042 |
| Haiku 4.5 | $0.00000 | $0.00021 |
Grade A, and why
step-back 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 3d 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.
What it actually says
Step Back
Momentum is a poor architect. Each patch on top of existing code feels like the safe, local choice, but together they pile up into complexity no one would have designed on purpose. When fixes start stacking, stop and look at the whole picture: is the goal still the same, and if you were starting fresh with what you know now, would you build it this way?
If the answer is no, don't push through quietly. Tell the user in a few lines: where things are heading, why that's drifting, the cleaner alternative, and what you recommend. Then let them decide. If the answer is yes, carry on with confidence. The goal is to catch the wrong turn early, not to second-guess every step.
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.
- 3d ago First seen · 19 lines · 0 tokens per session scan A a27b861ced69
step-back is a skill published in the GitHub repository alvinunreal/oh-my-opencode-slim (9,686 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 212 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 2026-10-18.
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