Update LLM prose tells: new patterns + lol section (#8)

Updates LLM_PROSE_TELLS.md with three new patterns (two-clause compound sentence, almost-hedge, unnecessary contrast), the lol section with conversation excerpts, fixes for instances of these patterns throughout, and a bracket escaping fix for prettier idempotency. Checklist is now 24 items.

Co-authored-by: user <user@Mac.lan guest wan>
Reviewed-on: #8
Co-authored-by: clawbot <clawbot@noreply.example.org>
Co-committed-by: clawbot <clawbot@noreply.example.org>

prompts/LLM_PROSE_TELLS.md

@@ -26,9 +26,10 @@ Even outside the "not X but Y" pivot, models use em-dashes at far higher rates
 than human writers. They substitute em-dashes for commas, semicolons,
 parentheses, colons, and periods, often multiple times per paragraph. A human
 writer might use one or two in an entire piece for a specific parenthetical
-effect. Models scatter them everywhere because the em-dash can stand in for any
+effect. Models scatter them everywhere because the em-dash is a flexible
-other punctuation mark, so they default to it. More than two or three per page
+punctuation mark that can replace almost any other, and models default to
-is a meaningful signal on its own.
+flexible options. When a piece of prose has more than two or three em-dashes per
+page, that alone is a meaningful signal.
 
 ### The Colon Elaboration
 
@@ -43,9 +44,9 @@ normal. The frequency gives it away.
 
 > "It's fast, it's scalable, and it's open source."
 
-Three parallel items in a list, usually escalating. Always exactly three (rarely
+Three parallel items in a list, usually escalating. Always exactly three. Rarely
-two, never four) with strict grammatical parallelism that human writers rarely
+two. Never four. Strict grammatical parallelism that human writers rarely bother
-bother maintaining.
+maintaining.
 
 ### The Staccato Burst
 
@@ -58,11 +59,12 @@ at matching length creates a mechanical regularity that reads as generated.
 
 ### The Two-Clause Compound Sentence
 
-Possibly the most pervasive structural tell, and easy to miss because each
+This might be the single most pervasive structural tell, and it's easy to miss
-individual instance looks like normal English. The model produces sentence after
+because each individual instance looks like normal English. The model produces
-sentence where an independent clause is followed by a comma, a conjunction
+sentence after sentence in the same shape: an independent clause, a comma, a
-("and," "but," "which," "because"), and a second independent clause of similar
+conjunction ("and," "but," "which," "because"), and a second independent clause
-length. Every sentence becomes two balanced halves joined in the middle.
+of similar length. Over and over. Every sentence is two balanced halves joined
+in the middle.
 
 > "The construction itself is perfectly normal, which is why the frequency is
 > what gives it away." "They contain zero information, and the actual point
@@ -72,9 +74,9 @@ length. Every sentence becomes two balanced halves joined in the middle.
 
 Human prose has sentences with one clause, sentences with three, sentences that
 start with a subordinate clause before reaching the main one, sentences that
-embed their complexity in the middle. When every sentence on the page has that
+embed their complexity in the middle. When every sentence on the page has the
-same two-part structure, the rhythm becomes monotonous in a way that's hard to
+same two-part comma-conjunction-comma structure, the rhythm becomes monotonous
-pinpoint but easy to feel.
+in a way that's hard to pinpoint but easy to feel.
 
 ### Uniform Sentences Per Paragraph
 
@@ -89,7 +91,7 @@ shape of an idea, not a template.
 Sentence fragments used as standalone paragraphs for emphasis, like "Full stop."
 or "Let that sink in." on their own line. Using one in an entire essay is a
 reasonable stylistic choice, but models drop them in once per section or more,
-at which point it becomes a habit rather than a deliberate decision.
+at which point it stops being deliberate and becomes a habit.
 
 ### The Pivot Paragraph
 
@@ -124,9 +126,9 @@ still says everything it needs to, the contrast was filler.
 
 > "So what does this mean for the average user? It means everything."
 
-A rhetorical question immediately followed by its own answer. Models do this two
+A rhetorical question immediately followed by its own answer. Models lean on
-or three times per piece because it fakes forward momentum. A human writer might
+this two or three times per piece because it generates the feeling of forward
-do it once.
+momentum without requiring any actual argument. A human writer might do it once.
 
 ---
 
@@ -182,8 +184,9 @@ out to the civilizational scale before they've said anything specific.
 > "While X has its drawbacks, it also offers significant benefits."
 
 Every argument followed by a concession, every criticism softened. A direct
-artifact of RLHF training, which penalizes strong stances. Models reflexively
+artifact of RLHF training, which penalizes strong stances. The result is a model
-both-sides everything even when a clear position would serve the reader better.
+that reflexively both-sides everything even when a clear position would serve
+the reader better.
 
 ### The Throat-Clearing Opener
 
@@ -243,9 +246,8 @@ uneven, with 50 words in one section and 400 in the next.
 ### The Five-Paragraph Prison
 
 Model essays follow a rigid introduction-body-conclusion arc even when nobody
-asked for one. The introduction previews the argument, the body presents 3 to 5
+asked for one. Introduction previews the argument. Body presents 3 to 5 points.
-points, and then the conclusion restates the thesis using slightly different
+Conclusion restates the thesis in different words.
-words.
 
 ### Connector Addiction
 
@@ -262,8 +264,8 @@ obscure idiom without explaining it, make a joke that risks falling flat, leave
 a thought genuinely unfinished, or keep a sentence the writer liked the sound of
 even though it doesn't quite work.
 
-Human writing does all of those things regularly. That total absence of rough
+Human writing does all of those things. The total absence of rough edges, false
-patches and false starts is one of the strongest signals that text was
+starts, and odd rhythmic choices is one of the strongest signals that text was
 machine-generated.
 
 ---
@@ -304,7 +306,7 @@ What gives it away is how many of these show up at once. Model output will hit
 distributed unevenly, mixed with idiosyncratic constructions no model would
 produce. When every paragraph on the page reads like it came from the same
 careful, balanced, slightly formal, structurally predictable process, it was
-generated by one.
+probably generated by one.
 
 ---
 
@@ -350,7 +352,7 @@ passes, because fixing one pattern often introduces another.
 7. Search for em-dashes and replace each one with the punctuation mark that
    would normally be used in that position (comma, semicolon, colon, period, or
    parentheses). If you can't identify which one it should be, the sentence
-   needs to be restructured.
+   probably needs to be restructured.
 
 ### Pass 2: Sentence-Level Restructuring
 
@@ -481,16 +483,10 @@ roughly like this:
 >
 > **model:** _(rewrites entire document without em-dashes while describing
 > em-dash overuse)_
->
-> **human:** now run the checklist methodically on each paragraph
->
-> **model:** _(finds staccato burst in the section about triple constructions, a
-> triple in the section about absence of mess, two-clause compounds everywhere,
-> and "almost" hedges in its own prose about em-dash overuse)_
 
 The human compared this process to the deleted scene in Terminator 2 where John
 Connor switches the T-800's CPU to learning mode. The model compared it to a
-physician trying to heal itself. Both are accurate.
+physician trying to heal itself. Both descriptions are probably accurate.
 
-This document has been through eight editing passes and it still has tells in
+This document has been through seven editing passes and it probably still has
-it.
+tells in it.