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 nataliacorrea03/claude-code-skills --skill plain-englishgit clone --depth 1 https://github.com/nataliacorrea03/claude-code-skillsWrote 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/nataliacorrea03/claude-code-skills/plain-english)<a href="https://agentmods.dev/skills/nataliacorrea03/claude-code-skills/plain-english"><img src="https://agentmods.dev/badge/skills/nataliacorrea03/claude-code-skills/plain-english/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/nataliacorrea03/claude-code-skills/plain-english"><img src="https://agentmods.dev/badge/skills/nataliacorrea03/claude-code-skills/plain-english.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 | $0.00163 | $0.00811 |
| Opus 5 | $0.00081 | $0.00405 |
| Sonnet 5 | $0.00033 | $0.00162 |
| Haiku 4.5 | $0.00016 | $0.00081 |
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 11d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plain English
What this is for
Users keep having to tell Claude to redo, rephrase, or "use plain English." The root cause usually isn't formatting. It's that the explanation is too long, buried in jargon, or explaining things they didn't need explained. They bounce off it and have to ask again.
This skill is the fast fix. The user fires it, you rewrite the thing so they actually read it.
What to do
Default target is the last thing you said. If the user points it somewhere else (an email, a doc, text they paste), rewrite that instead.
Rewrite it so:
- Lead with the answer. First line says the thing or what it means for them. No runway, no "so basically," no "great question."
- Cut the jargon. If a technical word is genuinely load-bearing, drop one plain phrase next to it in parentheses. Otherwise replace it. Assume the reader is smart but does not want to debug your internals.
- Shorter than you think. Most of these collapse to 2-4 sentences or a few bullets. If the original was ten lines, the rewrite is probably three.
- Drop what they can't act on. The caveats, the "it's worth noting," the background they didn't ask for. If it doesn't change what they do next, it goes.
- Keep it human. No filler, no corporate buzzwords, no "it's not X, it's Y" constructions, short sentences. If it reads like a LinkedIn post, it's wrong.
Then just give the clean version. Don't show the before, don't explain what you trimmed, don't apologize for the first one. They asked for the short version, so the response to "make it shorter" should not itself be long.
The instinct to carry forward
The bigger signal: if the user is invoking this, the last answer missed. Carry that into the rest of the session without being told. Default to shorter and plainer for the rest of the conversation. They can always ask for more depth. They rarely will.
Examples
Example 1, too technical
Before:
The cron routine fires via a scheduled trigger on a weekday cadence and the dedup logic keys off whether the thread already has a draft, so idempotency is preserved across overlapping invocations.
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.
- 11d ago First seen · 57 lines · 0 tokens per session scan A e7623ee4042f
plain-english is a skill published in the GitHub repository nataliacorrea03/claude-code-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 163 tokens to every session and 811 once invoked, about $0.0008 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.
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react-patterns
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content-engine
Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
article-writing
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
agent-carnet
Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.