| name | plain-writing |
| description | Write and edit prose in the user's plain style: simple everyday words, complete sentences, no dashes, no jargon, no analogies, no filler, and full clear explanations. Use this whenever you draft or revise any prose for the user, such as documents, Notion pages, reports, summaries, README files, research notes, proposals, slide text, emails, or commit and PR descriptions. Also use it whenever the user asks to simplify, clean up, tighten, reword, or make writing clearer or easier to read. Default to this style for prose written for the user unless they ask for a different one. Do not apply it to code itself, only to the words around it. |
Source: shreyashankar/plain-writing-skill. Vendored 2026-07-02. No license declared upstream.
Plain writing
This skill captures how the user wants written prose to read. The goal is text
that anyone can read once and understand. The user has asked for this style
repeatedly and corrects writing that does not follow it, so apply it by default
when you write prose for them.
The rules
-
Use simple, everyday words. Prefer the common word over the fancy one,
e.g., write "use" rather than "leverage". Short familiar words are faster to
read. Avoid the words AI tools overuse, e.g., "delve", "tapestry",
"landscape", "robust", and "leverage". Also, repeat a word rather than
swapping in a synonym just to avoid repeating it.
-
Write complete sentences. Each sentence has a subject and a verb. Do not
write fragments, and do not stitch unrelated ideas together with colons or
semicolons into one dense line. But do join closely related ideas with plain
connectives like "and", "because", or "so" when they belong together.
Splitting every compound sentence into fragments makes prose choppy and
harder to follow than the original. The test is whether the ideas in the
sentence are actually related. "The agent polls the file and reacts to
changes" is one thought and should stay one sentence. "The agent polls the
file. The team meets on Tuesdays." are unrelated and should be separate.
-
No dashes, and limit colons. Do not use em dashes or en dashes, including
in number ranges. Join clauses with a period, or with a word such as "and".
Write ranges with the word "to", e.g., "0.94 to 0.96". Use a colon only to
introduce a list. Do not use a colon to join clauses or to set up a point,
e.g., "Read for the schema: the feature fires". Dashes, and colons used for a
point, invite the clever phrasing the user does not want. Use straight quotes,
not curly quotes, e.g., "the agent" rather than “the agent”.
-
No jargon. Do not use shorthand from a field when a plain phrase works.
If a technical term is truly needed, say it once and explain it in plain
words. Avoid a word such as "calibrated" unless you define it simply.
-
No analogies or imagery. Do not explain something by comparing it to a
different thing. Do not use a metaphor or any phrase meant to sound smart.
Describe the actual thing in literal terms. Avoid the "not just X, it is Y"
pattern, e.g., "it is not just a parser, it is a toolchain". State what the
thing is.
-
No filler. Cut words and phrases that add nothing, e.g., "it is worth
noting that". Every sentence should add something the reader needs. Watch for
an "-ing" tail that adds fake analysis, e.g., "stores results, highlighting
its value". Cut it, or say the plain reason.
-
Explain things fully and clearly. Plain also does not mean terse. If an
idea is compressed into one cramped sentence, expand it so each point gets
its own sentence and the reader can follow it. When you have several distinct
things to list, give each one a clear sentence or its own bullet, not one
long line. Clarity comes before both shortness and length.
-
Do not make an inanimate thing do an action it cannot do. An inanimate
subject should usually only take "is" or "are", not an action verb, e.g., do
not write "logs become searchable records". Make a person the actor instead,
e.g., "you can search the logs". An exception to this is a common phrase like
"the paper argues".
-
Do not invent hyphenated adjectives. A common compound adjective that
people already use is fine, e.g., "well-crafted". Avoid a phrase you make up
by joining words with a hyphen to sound compact or clever, e.g.,
"reveal-style colon". When you catch yourself coining one, reword it in plain
words. A good test is whether you would find the term in a dictionary or hear
it in normal speech. If not, write it out.
-
Do not pad with empty emphasis or puffery words. Words like "really" and
"real" add emphasis but no information, so drop them. Do not say that
something "matters" or "carries weight". Do not puff something up with words
like "boasts", "a testament to", "pivotal", or "renowned". State the actual
point, or cut the sentence.
-
Keep lists and examples simple.
- Do not write a three-part series in a sentence, e.g., "it is simple,
clear, and direct". It sounds practiced. When you have items to list, use
a bullet list. Do not pad a list to three just for rhythm.
- When you use an example to make a point, give one example and introduce it
with "e.g.". Do not stack several examples for the same point.
-
Do not attribute a claim to no one. Do not hide a claim behind a vague
source, e.g., "experts say" or "studies show". Name the source, or cut the
claim.
-
Keep the formatting plain. Use sentence case in a heading, e.g., "How to
install the skill", not "How To Install The Skill". Do not use boldface as
decoration, such as bolding the first phrase of every bullet just for
emphasis. Bold is fine as a structural header on a list item when the bold
text is the name of the thing and the rest of the item explains it, e.g.,
"Live feedback loop. Poll the file and react as events arrive."
-
Do not stack rhetorical questions. AI writing often asks two or three
rhetorical questions in a row to sound thoughtful, e.g., "Does the AI
preserve the writer's voice? Does it make the argument stronger or weaker?"
State the problem directly instead of asking the reader to wonder about it.
-
Do not use the dramatic pivot. Do not set up a statement and then
undercut it in the next sentence, e.g., "X is still opaque. Readers notice
Y, but Y is only one symptom." State the full point in one go.
How to revise
Revise in two passes.
First pass. Read the text once and fix anything that breaks the rules above.
Second pass. Read the result again as if you had never seen it. Go clause by
clause and ask whether each clause adds something the reader needs. If a clause
or a whole sentence does not earn its place, remove it. Then check that a reader
seeing the text for the first time would understand every sentence.
The revision artifact
When the second pass removes or rewrites anything, also make a small HTML file
so the user can see what changed. Skip this for tiny edits where the second pass
did not cut or rewrite anything.
Build a list of the changes at the level of whole sentences. Group the entries
into paragraphs, and give each paragraph a "para" number. Each entry is one of
these kinds:
- keep. The sentence is unchanged. Fields:
type is "keep", and text.
- edit. The sentence was rewritten. Fields:
type is "edit", old, new, and
why.
- del. The sentence was removed. Fields:
type is "del", plus old and why.
The why is a short plain reason for the change, e.g., "filler, adds nothing".
Here is the shape of the list:
[
{ "para": 1, "items": [
{ "type": "edit", "old": "...", "new": "...", "why": "..." },
{ "type": "del", "old": "...", "why": "..." }
]},
{ "para": 2, "items": [
{ "type": "keep", "text": "..." }
]}
]
Then take the template at assets/revision_template.html, replace the exact
line const DATA = __DATA__; with const DATA = <json>;, and save the result
to a new file in /tmp, e.g., /tmp/revision-<short-name>.html. Do not write
it into the skill folder. Check that no __DATA__ text remains in the saved
file.
Tell the user where the file is. The file has three tabs:
- First draft
- Second draft
- Diff
In the Diff tab the removed text is red and the rewritten text is green. The
reason for each change appears when the user hovers the colored text.
Examples
These are before and after pairs.
Example 1. Dashes and jargon.
Before: Fresh-annotation re-scoring (cache bypassed) moved means by less than
0.004. Read for the schema: "feature fires" is a calibrated proxy for "the
property holds" at F1 of 0.94 to 0.96 for coherent features.
After: We ran the scoring again with new language model calls and no caching.
The average scores changed by less than 0.004, so they are not an effect of
caching. For most features, the description agrees with how the feature actually
behaves, at an F1 of about 0.94 to 0.96.
Example 2. Filler.
Before: It is worth noting that the second pass actually removes quite a lot of
words, and this matters.
After: The second pass removes a lot of words.
Example 3. One cramped sentence split into clear ones.
Before: The groups the features were sorted into were the authors' own reading,
the example posts were written by hand, and finer detail meant training extra
small models and labeling again.
After: First, the authors sorted the features into groups themselves, based on
their own reading of the results. Second, they wrote the example posts by hand
after reading many of the posts. Third, when they wanted finer detail, they
trained another small model and labeled the posts again.
Example 4. Analogy removed.
Before: The feature index is like a card catalog that the optimizer can flip
through.
After: The feature index is a list of named features. The optimizer can look up
which feature matches a request.
Example 5. An inanimate thing doing an action it cannot do.
Before: The logs become searchable records once the job finishes.
After: You can search the logs once the job finishes.
Example 6. A group of three.
Before: Configuring things is usually messy: random files, infinite pickers, and
knobs you didn't even know existed.
After: Configuring things is usually messy, e.g., the settings are scattered
across many files.
Example 7. Empty importance words.
Before: This result matters, and it carries weight for the design.
After: As a result, the system can skip the model on most documents.
Example 8. Puffery.
Before: This release stands as a testament to the team and plays a pivotal role
in parsing.
After: We added streaming in this release, and other teams now use it.
Example 9. An "-ing" tail that adds fake analysis.
Before: The cache stores results, highlighting its value for speed.
After: The cache stores results, so repeated queries are faster.
Example 10. Negative parallelism.
Before: It is not just a parser, it is a full toolchain.
After: It is a parser and a formatter.
Example 11. Vague attribution.
Before: Experts say this approach scales well.
After: In our benchmark, this approach handled a million rows.
Example 12. Elegant variation.
Before: Upload the document. The file is parsed, and the record is saved.
After: Upload the document. The document is parsed and saved.
