Plain Language Skill: Explain Without Dumbing Down

Precision-Preserving Plain Language is an AI skill that rewrites dense technical material for a specific reader without hollowing out the truth. It keeps the numbers, the conditions, the uncertainty, and the caveats that could change a decision — and makes everything around them readable.

The two failures it's built to avoid

Most attempts to simplify technical writing fail in one of two opposite ways.

Untranslated. The jargon stays. The reader nods along and walks away with nothing.

Hollowed out. Every precise term gets swapped for a vague one. The result is easy to read and impossible to act on — and worse, it leaves the reader confident about something they've misunderstood.

The skill's rule is to simplify the language and the route through the idea, never the truth itself. That's the difference between plain language and just shorter words.

What it does

It works in six deliberate steps, each with a clear finish line.

1. Find out what the reader already has. An executive, a junior engineer, and a customer need three different explanations of the same system. The skill establishes what the reader knows that's adjacent, what they'll do with the explanation — decide, use, approve, or just follow along — and how deep they need to go.

2. Find the load-bearing idea. Most explanations fail by treating everything with equal weight. The skill looks for the one idea that makes the rest follow. For a caching layer, that's we keep a copy of the answer so we don't redo the work — and eviction, expiry, and invalidation become easy consequences.

3. Handle each piece of jargon on purpose. Every term gets one of three treatments: replace it when a plain phrase means the same thing, teach it when the reader will meet it again, or cut it when it doesn't serve this reader. It never leaves a term undefined because it seems obvious — the curse of knowledge makes that difficulty invisible to experts.

4. Use analogies, then close them. An analogy that transfers the wrong structure creates a misconception harder to fix than ignorance. So every analogy comes with where it breaks down.

5. Rebuild the sentences. Buried verbs (nominalisations like "perform an evaluation of" instead of "evaluate"), passive voice that hides who does what, stacked modifiers, and abstract claims with no example all get rewritten.

6. Keep the precision that matters. "Faster" is not a simpler version of "about 3x faster on reads". "We think" must not become "it is". And "cannot" stays distinct from "does not yet".

Then it checks its own work twice: where would this reader stop and ask a question, and is there anything here that would let them confidently do the wrong thing?

A quick before and after

This is our illustration of the approach, not output copied from the skill.

Before: "Implementation of the caching layer resulted in a significant reduction in p95 latency for read operations under typical load conditions."

After: "We added a cache — a stored copy of recent answers, so the system doesn't redo the same work. Under typical traffic, the slowest 5% of read requests now finish noticeably faster; engineers call that measure p95 latency. The result is for reads under normal load, so it doesn't yet tell us anything about writes or traffic spikes."

The vague word "significant" stays vague, because the original gave no number. The conditions — reads, typical load — survive, and the reader learns the one term they'll hear again.

What it won't do

The Taku version is precise about its scope. It is not grammar correction, removing "AI-sounding" writing, translation, email drafting, generic summarisation, or fact checking. It works only from the material and reader context you provide. It doesn't browse, send, publish, write files, contact anyone, or claim that the source material is true.

When to reach for it

• A technical decision needs approval from people who won't read the design doc
• An incident write-up has to make sense to customers
• Onboarding docs keep generating "what does this mean?" questions
• A specialist report is going to a general audience with real decisions to make
• You're the expert, and you suspect you can no longer see what's hard about your own topic

What's actually inside

One skill file. No connectors, no browsing, no API keys. Upstream, it's part of a collection of more than 60 agent skills covering the software lifecycle — debugging, code review, testing, security, data, DevOps, and documentation — and it sits in the documentation group.

Setting it up

The GitHub route. The skill lives in skills/docs/plain-language in a public repository. Copy that folder into your AI tool's skills directory and restart the tool. The repository also includes an evaluation file for the skill, which is useful if you want to test changes to it.

The Taku route. Open it in Taku, paste the material, and say who the reader is.

Who made it

Written by Arjun Prabhulal and published under the MIT license in the agent skills repository. Arjun Prabhulal also published it to the Taku marketplace, where the listing adds the explicit scope limits described above.

You don't need to be technical to use this

You don't need to understand GitHub, Codex, or Claude Code to use this skill. Those are the usual homes for AI skills, and they assume you're comfortable with repositories — which is ironic for a skill whose whole job is making technical material accessible.

Taku is a desktop app built for exactly that: find something useful someone already made, open it in your own workspace, and use it with no setup. Browse the free app library for more, or read what Claude skills are for the background. If the dense material is a metrics report, the SaaS Metrics Health Check produces one worth explaining.

Download Taku and make your next technical explanation land. Taku is in Beta, and the Mac app is available now; there's also an experimental, unsigned Windows build for Windows 10 and 11.

FAQ

What does the Precision-Preserving Plain Language skill do?

It rewrites technical material for a specific reader: it identifies their starting knowledge and purpose, finds the core idea, handles jargon deliberately, closes analogies, rebuilds dense sentences, and preserves numbers, conditions, uncertainty, and caveats.

Is it just a summariser?

No. Summarising makes text shorter; this skill makes it understandable for a particular reader while keeping the details that could change a decision. It explicitly isn't a generic summariser.

Does it check whether the material is correct?

No. It isn't a fact checker and doesn't claim the source is true. It explains what you give it.

Does it translate?

No. It works within the same language.

Is it free?

The skill is free and open source. You need an AI tool that can run it. For what Taku costs, see pricing.

Key points

• Rewrites technical material for a named reader without losing precision.
• Avoids two failures: untranslated jargon and hollowed-out vagueness.
• Replaces, teaches, or cuts each term, and states where every analogy breaks.
• Keeps numbers, conditions, uncertainty, and decision-changing caveats.
• Not grammar correction, translation, summarisation, or fact checking.