Borrowing it
Nothing to install: this file belongs to Benkapner/claude-code-basecamp. 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/Benkapner/claude-code-basecamp/main/.claude/commands/explain-simple.mdgit clone --depth 1 https://github.com/Benkapner/claude-code-basecampWrote 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/benkapner/claude-code-basecamp/explain-simple)<a href="https://agentmods.dev/commands/benkapner/claude-code-basecamp/explain-simple"><img src="https://agentmods.dev/badge/commands/benkapner/claude-code-basecamp/explain-simple/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/benkapner/claude-code-basecamp/explain-simple"><img src="https://agentmods.dev/badge/commands/benkapner/claude-code-basecamp/explain-simple.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 | $0.00032 | $0.00733 |
| Opus 5.5 | $0.00013 | $0.00293 |
| Sonnet 5.5 | $0.00006 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
explain-simple 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain Simple
Explain code like you're talking to a smart 15-year-old who has never programmed. No jargon. No implementation details. Just what it does, why it exists, and how it fits into the bigger picture.
Instructions
1. Read the Target
- Read the file or folder specified in $ARGUMENTS
- If it's a folder, read the key files to understand the overall purpose
- If it's a single file, understand what it does in context
2. Explain Simply
Write an explanation that follows these rules:
- No jargon. Don't say "API endpoint", say "a way for other programs to ask for data." Don't say "parses JSON", say "reads a data file."
- Use analogies. Compare to real-world things. A pipeline is like an assembly line. A config file is like a settings menu. A function is like a recipe.
- Start with WHY. Before explaining what it does, explain why someone built it. What problem does it solve?
- Short sentences. If a sentence has a comma, consider splitting it.
- No code. Don't show code snippets. If you must reference something technical, explain it immediately.
- Structure it: What is this? → Why does it exist? → How does it work (simply)? → How does it connect to the rest of the project?
3. Format
What is this?
[1-2 sentences]
Why does it exist?
[1-2 sentences about the problem it solves]
How does it work?
[3-5 bullet points, simple language, analogies welcome]
How does it fit in?
[1-2 sentences about where it sits in the bigger project]
Example
For a file like scripts/fetch_data.py:
What is this?
This is a script that downloads records from an external API.
Why does it exist?
We want to know what everyone is working on, but that information is scattered across
thousands of tickets. This script collects all of them automatically.
How does it work?
- It goes through a list of people and looks up their tickets, one person at a time
- It's polite about it — if the server says "slow down", it waits before asking again
- For each ticket, it also grabs the parent project it belongs to, so we get the full picture
- It saves everything into one big file that other scripts can use later
How does it fit in?
This is step 2 of 4. First we get the list of people (step 1), then this script gets their
work data, then another script summarizes what each person does (step 3), and finally
everything gets organized into a report (step 4).
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 · 81 lines · 32 tokens per session scan A 1b9135a57937
explain-simple is a command published in the GitHub repository Benkapner/claude-code-basecamp (18 stars, last pushed 27d ago), licensed MIT. It adds 32 tokens to every session and 733 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other commands, from other repositories
spec-forge
Use when generating software specifications — full chain ([Idea]→[Decompose]→[PRD]→SRS→Tech Design→Review) or individual documents.
propagate
Use after editing an upstream doc (PRD/SRS/tech-design/feature-spec) to propagate changes downstream and keep the entire doc chain consistent.
analyze
Use when analyzing a document collection to map themes, find conflicts, gaps, and redundancies — generates landscape analysis report.
review
Use when reviewing spec-forge generated documents for quality, completeness, and consistency — auto-fixes issues if found.
audit
Use when auditing existing project docs for quality, completeness, and code alignment — generates findings report with fix recommendations.
srs
Use when writing the requirements a team will be held to — an SRS, requirements spec, or a requirements document for an implementation team or vendor (IEEE 830, with traceability and acceptance criteria).