amazon-writing
Skill1 starsChanged 4 months agoMITView on GitHub
Write in the plain, data-first style from Amazon's internal "Write Like an Amazonian" guide - short sentences, numbers instead of adjectives, no weasel words, every line passes the "so what" test. Use this for any prose the user will read or send - docs, design docs, status updates, emails, Slack messages, PR descriptions, postmortems, replies to a manager - and when editing or tightening their writing, even if they never mention Amazon or style. Also use it when the user complains that writing sounds like AI, is fluffy, or "gives them an ick".
In this repository9 other files it gives its agents.
- add-umami-analyticsSkill
- brand-namingSkill
- creativity-rejectionismSkill
- explain-diff-notionSkill
- explain-diffSkill
- explain-notionSkill
- explainSkill
- fetch-paperSkill
- ml-paper-writingSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 29
Pass the "so what" test: After each sentence, ask what the reader learns or can do now.
- Distinctive: line 35
Do not invent data to satisfy rule 2.
- Distinctive: line 36
use the numbers the user gave, all of them, exactly.
- Very specific: line 37
if a claim needs a number you don't have, state the claim plainly without the intensifier ("latency improved"), or leave a visible gap like …
- Distinctive: line 57
Write as the user would. If they will send it as themselves, use first person and their register.
- Distinctive: line 67
Every number from the user included, with its unit?
≈ “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
Amazon-style writing
Readers of work writing are trying to make a decision. Every rule below exists to help them make it faster: claims they can check, sentences they can parse in one pass, and nothing they have to skim past.
This style also happens to remove most of what makes text read as machine-written. Intensifiers, hedges, tidy triplets and summary paragraphs are the habits readers recognize and distrust. Writing that follows these rules does not sound like an LLM, because it does not sound like anything; it just states facts.
The rules
- Keep every sentence under 30 words. One idea per sentence. Cut wordy phrases:
- "due to the fact that" → "because"
- "totally lacked the ability to" → "could not"
- "in order to" → "to"; "at this point in time" → "now"
- Replace adjectives and adverbs with data. Adjectives are imprecise and do not help anyone decide.
- "We made the performance much faster" → "We reduced server-side p90 latency from 10 ms to 1 ms"
- "This will make the launch extremely successful" → "This will increase output by 2.5%"
- Eliminate weasel words. "Nearly all", "significantly", "many", "most", "a lot", "substantially" hide the number. Use the number.
- "Nearly all customers" → "87% of Prime members"
- "Significantly better" → "+25 basis points"
- Give every metric its unit, period and baseline. "Up 40%" means little. "Unit sales rose 40% in Q4 2011 compared to Q4 2010" means something.
- Pass the "so what" test. After each sentence, ask what the reader learns or can do now. If nothing, cut it or add the consequence.
- Answer questions with one of four answers. Yes. No. A number. "I don't know; I'll follow up by
<when>." Put the answer first, then the one condition or caveat that matters.
When the user did not give you the number
Do not invent data to satisfy rule 2. Making up a figure is worse than an adjective because it looks checkable. Instead:
- use the numbers the user gave, all of them, exactly;
- if a claim needs a number you don't have, state the claim plainly without the intensifier ("latency improved"), or leave a visible gap like
[p99 before/after?]and mention it in one line to the user.
Things that read as AI filler - leave them out
These add length, not information. Readers notice them and stop trusting the rest:
- Openers and closers: "Great question", "I hope this helps", "In summary", "Overall", "Let me know if you have any questions", "This document outlines…"
- Hype and filler words: delve, leverage, robust, seamless, crucial, pivotal, game-changer, cutting-edge, streamline, unlock, empower, "it's worth noting", "in today's fast-paced world"
- Sincerity markers: honestly, genuinely, truly, straightforward
- Reflexive triplets ("fast, reliable, and scalable") when the facts don't come in threes
- "Not just X, but Y" and other set-up/pay-off constructions
- Em dashes used as decoration; use a period or a comma
- Headings, bold and bullets on a message that is three sentences long
- Restating the question before answering it
Shape
- Lead with the answer or the decision. Background comes after, if at all.
- Match length to the ask. A status update is a few sentences, not a report. A Slack reply is one to three lines.
- Prose for reasoning; bullets only for parallel items (a list of risks, a list of steps).
- Write as the user would. If they will send it as themselves, use first person and their register.
Editing someone's draft
Apply the rules in place and keep their meaning and voice. Don't add claims they didn't make. If a vague phrase needs a number you don't have, mark it ([number?]) rather than guessing. Briefly list the kinds of changes you made only if they ask.
Quick self-check before sending
- Any sentence over 30 words? Split it.
- Any "very", "significantly", "nearly", "extremely", "much"? Replace with the number or delete.
- Every number from the user included, with its unit?
- First sentence states the point?
- Any sentence that fails "so what"? Cut it.
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.

