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 agentmods add commands/reggiechan74/cc-plugins/plain-englishgit clone --depth 1 https://github.com/reggiechan74/cc-pluginsWrote 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/reggiechan74/cc-plugins/plain-english)<a href="https://agentmods.dev/commands/reggiechan74/cc-plugins/plain-english"><img src="https://agentmods.dev/badge/commands/reggiechan74/cc-plugins/plain-english.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 | $0.00016 | $0.02506 |
| Opus 5 | $0.00008 | $0.01253 |
| Sonnet 5 | $0.00003 | $0.00501 |
| Haiku 4.5 | $0.00002 | $0.00251 |
Grade A, and why
plain-english 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plain-English Companion Generator
Purpose: Take a mathematically dense, symbol-heavy, or conceptually difficult document and produce a companion version that adds plain-English explanation callouts after complex sections. The original text is preserved by default.
Architecture: Uses a chunked processing pipeline — each section is processed independently to avoid context overflow on large documents. No single agent call needs to hold the full source + full output.
Instructions
When this command is invoked:
Step 1: Parse Arguments
- First argument (required): path to the source document
- Second argument (optional): output path. Default:
<source_name>_plain_english.mdin the same directory
If no file path is provided, use AskUserQuestion to request one.
Step 2: Read and Analyze the Source Document
Read the source document. Perform a quick structural analysis:
- Count sections/headings
- Identify math-heavy sections (LaTeX
$...$,$$...$$, Greek symbols, formal notation) - Identify conceptually dense sections (formal definitions, domain-specific jargon, multi-layered abstractions)
- Estimate total complexity (light, moderate, heavy)
Report to the user:
Analyzed: <filename>
- <N> sections, <M> lines
- Math density: <light/moderate/heavy> (<count> formulas detected)
- Estimated output: ~<N> callout insertions
Step 3: Interview the User
Use AskUserQuestion to ask the following questions one at a time. Each question should include the multiple-choice options and a brief note on trade-offs.
If at any point the user says "defaults", "defaults are fine", "just go", "skip", or similar — stop the interview and use defaults for all remaining questions.
Question 1 — Audience:
Who is the primary audience for the plain-English version?
A) Executives/decision-makers — focus on "so what?" and strategic implications
B) Practitioners/operators — focus on "how do I use this?" with concrete examples
C) Smart generalist (grade 12 level) — focus on intuition and everyday analogies
D) All of the above — layered callouts serving all three
E) Custom — describe your audience
(Default: C)
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 · 300 lines · 16 tokens per session scan A 742d82b78f4e
plain-english is a command published in the GitHub repository reggiechan74/cc-plugins (6 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 2,506 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 2026-08-31.
Other commands, from other repositories
organize-files
Organize and rename files based on content analysis.
brand-generate
Generate an on-brand document from a saved Brand Profile.
tree
Show file tree with sync status indicators showing which files are indexed, modified, new, or deleted.
cti-report
Render case deliverables — relationship graph (PNG/SVG/Mermaid) and a polished PDF/DOCX assessment. Usage: /cti-report [--graph|--pdf].
ppt-image2-editable-rebuild
Rebuild image2 or imagegen reference slides as editable PowerPoint decks.
convert
Convert a file to Markdown using markitdown.