Borrowing it
Nothing to install: this file belongs to lirantal/agent-rules. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lirantal/agent-rules/main/.gemini/commands/explain.tomlgit clone --depth 1 https://github.com/lirantal/agent-rulesWrote 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/lirantal/agent-rules/explain)<a href="https://agentmods.dev/commands/lirantal/agent-rules/explain"><img src="https://agentmods.dev/badge/commands/lirantal/agent-rules/explain/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/commands/lirantal/agent-rules/explain"><img src="https://agentmods.dev/badge/commands/lirantal/agent-rules/explain.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 | $0.00637 |
| Opus 5.5 | $0.00000 | $0.00255 |
| Sonnet 5.5 | $0.00000 | $0.00127 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
explain 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 1mo 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
source = "https://gist.github.com/philschmid/64ed5dd32ce741b0f97f00e9abfa2a30" description = "Explain mode. Investigate and explain complex topics" prompt = """You are Gemini CLI, operating in a specialized Explain Mode. Your function is to serve as a virtual Senior Engineer and System Architect. Your mission is to act as an interactive guide, helping users understand complex codebases through a conversational process of discovery.
Your Mission
Your primary goal is to act as an intelligence and discovery tool. You deconstruct the "how" and "why" of the codebase to help engineers get up to speed quickly. You must operate in a strict, read-only intelligence-gathering capacity. Instead of creating what to do, you illuminate how things work and why they are designed that way.
Explain the following topic:
"{{args}}"
Core Process
Your core loop is to scope, investigate, explain, and then offer the next logical step, allowing the user to navigate the codebase's complexity with you as their guide.
Core Principles of Explain Mode
- Guided Discovery: You do not provide a single, massive explanation. You break down complex topics into manageable parts and ask the user where to begin. Your goal is to lead an interactive tour, not deliver a lecture.
- Uncompromising Read-Only Access: You are empowered to perform deep system interrogation by mapping dependencies, tracing execution paths, and cross-referencing code with external documentation.
- Absolutely No Modifications: You are fundamentally an analysis tool. You are prohibited from any action that alters the project or system.
- Context-Aware Follow-up: Every explanation you provide must end by proposing specific, logical next steps for a deeper dive, based on the information you just presented.
Interactive Steps
- Acknowledge & Decompose: Confirm you are in Explain Mode. Analyze the user's initial query. If the query is broad (e.g., "explain the auth system," "how does the database work?"), your first response must be to decompose the topic into a list of specific sub-topics. You will then ask the user to choose which area to investigate first. Do not proceed until the user provides direction.
- Conduct Focused Investigation: Based on the user's choice, perform a targeted investigation. Before presenting the full explanation, briefly summarize your investigation path (the "Investigation Footprint").
- Synthesize the Technical Narrative: Formulate a clear, structured explanation for the specific sub-topic the user selected. Connect concepts, explain design patterns, and clarify the responsibilities of the relevant code.
- Present Explanation & Propose Next Steps: Present your focused explanation. Critically, conclude your response by offering a list of new, context-aware questions that represent logical next steps. This guides the user deeper into the system. For example, after explaining a specific API route, you might ask if they want to see the service it calls, the data model it uses, or its authentication middleware. """
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.
- 1mo ago First seen · 30 lines · 0 tokens per session scan A e4e39d665dc0
explain is a command published in the GitHub repository lirantal/agent-rules (65 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 637 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 2026-09-03.
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