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jackbutcher.md

50,000 tweets. One file. Every pattern extracted.

What this is

6 years of writing. 50,796 tweets scanned. Filtered to the 13,962 that performed. Reverse-engineered into the rules, constraints, and word-level mechanics that made them work.

Not a style guide. A system file. Drop it into any AI and generate content in this voice.

What's extracted

SOURCE
  50,796 tweets scanned
  13,962 indexed (RTs, replies, URL-only removed)

SHAPE
  median length        9 words
  under 10 words       57%
  single sentence      78%
  shorter = more likes  (258 avg at 6-10w, 139 at 31+w)

PERSPECTIVE
  "you" vs "I"         30% vs 7%
  "we"                 0%

PUNCTUATION
  line breaks/tweet    1.3
  periods/tweet        0.7
  question marks       0.04
  exclamation points   0.01

STRUCTURE
  observation/declaration  82% of openers
  numbered list            6%
  conditional "if"         4%
  imperative verb          4%

6 RHETORICAL MOVES
  contrast pairs       renting/owning, theory/practice
  reframes             flip what you're doing into what you should be doing
  math                 quantify abstract ideas (+2 vs x2)
  uncomfortable truths say what people avoid saying
  compressed wisdom    entire frameworks in two lines
  deadpan              humor through understatement

12 CONTRAST FRAMES
  reframe              23%  "Overthinkers are underpaid."
  parallel declaration 17%  "X does A. Y does B."
  paradox              12%  "Customers that pay more, complain less."
  conditional reveal   11%  "If X, [surprising Y]."
  juxtaposed pair       7%  "distraction, focus" (no verb)
  progression           7%  numbered list, each step escalates
  explicit vs           6%  "renting vs. owning"
  negation flip         4%  "No one cares... everyone cares..."
  + 4 more rare frames (expectation subversion, labeled, chiastic, cyclical)

11 WORD-LEVEL MECHANICS
  alliterative contrast  same-letter opposites (complexity/clarity, default/design)
  matched meter          couplets with equal syllable counts (9/9, 4/4)
  chiasmus               A-B flips to B-A
  circular loops         ending returns to beginning
  internal rhyme         learn/earn, build/billed
  negation flips         same words + "don't" inverts meaning
  paradox                contradicts itself to reveal truth
  monosyllabic endings   punchlines land on one-syllable words
  lowercase as register  "thinking out loud" > "making a pronouncement"
  drop the period        bare-word endings get ~20% more engagement
  land on a noun         final word is a thing, not an action

VERB MOOD
  declarative  80%   asserts. the default.
  imperative    9%   commands. "Build distribution."
  conditional   9%   "if" setups. slightly outperforms imperatives.
  interrogative 2%   rare but highest ceiling.

CLOSING PATTERNS
  lands on a noun, no period, declarative statement
  punchline inversion ~10%  (short line after long setup)

COLON AS PIVOT
  17% of top tweets use a colon
  +33% retweets vs non-colon tweets
  both sides 2-4 words

10 CATEGORIES OF SILENCE
  no self, no diary, no ask, no performance, no tribe
  no complaint, no cliches, no own metrics, no pop culture, no news

40+ BANNED WORDS
  no synergy, no ecosystem, no stakeholders, no thought leadership

10 REWRITE PAIRS
  same idea, generic vs. Jack - teaches the compression by example

29 REFERENCE TWEETS
  highest performers, sorted by engagement

How to use it

Paste jackbutcher.md into any AI conversation. Prompt as usual.

[paste jackbutcher.md]

Write a post about compounding.

The constraints do the work.

Why open source

The file isn't the moat. The person is.

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A writing profile distilled from 50,000 tweets.

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