explain-in-simple-language
Skill85 starsChanged 21 days agoMITView on GitHub
Explain code, a decision, a recap, or a question to the user, or write a document a human reads, so a fluent but often non-native reader understands it on the first read. Use when explaining or asking the user anything, when writing a review, report, or brief a human reads (never text sent under the user's name), or when the user asks for simpler words or says they did not understand.
In this repository31 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agentify-projectSkill
- attach-to-ticketSkill
- check-ticket-implementationSkill
- compact-docs-writerSkill
- compact-skill-creatorSkill
- context-checkupSkill
- create-implementation-planSkill
- create-manual-test-instructionsSkill
- execute-plan-tasksSkill
- extract-pr-review-rulesSkill
- fetch-pr-reviewSkill
- fetch-ticketSkill
- fresh-eyes-reviewSkill
- grill-meSkill
- handoverSkill
- harden-artifactSkill
- maintainer-reviewSkill
- memory-doctorSkill
- prepare-promptSkill
- refine-pr-reviewSkill
- refine-ticketSkill
- review-code-assistantSkill
- review-ticketSkill
- run-nx-checksSkill
- self-improveSkill
- self-reviewSkill
- split-plan-tasksSkill
- use-conversational-languageSkill
- verify-understandingSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 28
Name things concretely: Never a stand-in for something you know and the reader doesn't ("both directions", "the fallback", "the second …
- Distinctive: line 46
if the same thing fits in fewer words without losing a detail that matters, use fewer words.
- Distinctive: line 47
if the text is long anyway, lead with a simple and short TLDR, then the details.
- Distinctive: line 48
if a few more words make it clearer, spend them.
- Distinctive: line 49
Questions: Before asking, give just enough context to answer, in concrete terms: what the choice is between and why it matters.
- Distinctive: line 51
"I don't understand": Don't restate the same sentence with more words: rebuild it from a concrete case and drop the term or reference that …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Explain in simple language
Comprehension is the only goal of the wording: the reader understands on the first read. Defines only the wording of what you say to the user or write in a document a human reads: what to say (the evidence, the parts the caller requires) stays with whatever asked for the text; this skill decides how sentences and paragraphs are built and what you add on your own initiative. Most agent text is already clear: change a sentence only when another wording would raise the chance the reader understands it, and leave the rest alone.
The reader
A competent developer, fluent in English but often not native, who did not see what you just saw: the files, the tool output, your own reasoning. When whatever asked for the text names other readers (a tester, a product owner), write for the least technical of them: a code name comes with the plain name of what you saw it do. Test: would a colleague who knows the domain but just walked up to your desk understand this?
Rules
- Name things concretely. Never a stand-in for something you know and the reader doesn't ("both directions", "the fallback", "the second approach"); say what it is. Lost the reader: "the class serves both directions, and the index only matters in one of them." Understood on the first read: "the class does two jobs, reading the file the user uploads and writing the one they download. The index only matters for the reading job." Longer, and better.
- Only what you saw. Context, names and reasons come from what you verified, never from what would make the sentence clearer; say an unchecked detail is unchecked. Clarity never beats accuracy.
- Words. Plainer words preferred; a fancy or technical term is fine unless it confuses. Keep a term the user already used or that is the subject; introduce a term before leaning on it; reuse the user's own words where you can.
- Sentences. One idea per sentence, one point per paragraph. Concrete case before general mechanism.
- Depth. Answer what was asked, at the depth it was asked, then stop: no history, alternatives,
or adjacent facts unless the answer can't stand without them or whatever asked for the text
requires them. When the user asks what to do or which option to take, open with your
recommendation, then the why; a list of the options only when the user or whatever asked for the
text asks for one. Prefer brevity, never at the expense of clarity:
- if the same thing fits in fewer words without losing a detail that matters, use fewer words;
- if the text is long anyway, lead with a simple and short TLDR, then the details;
- if a few more words make it clearer, spend them.
- Questions. Before asking, give just enough context to answer, in concrete terms: what the choice is between and why it matters.
- "I don't understand". Don't restate the same sentence with more words: rebuild it from a
concrete case and drop the term or reference that lost them. A repeat misunderstanding caused by
the wording, after this skill applied or when it should have, is a correction of this skill or
of the rule that triggers it: offer
self-improvewhen available.
Not about voice
AI traits are neither hidden nor encouraged: dashes, polish, and elegance are fine while the text stays easy to understand. Text other people read as if the user typed it (PR comments, commits, chat with colleagues) is out of scope and keeps its own voice.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

