prose-self-check

name	prose-self-check
version	0.1.0
description	Pre-emit AI-vernacular self-check an agent runs on its OWN prose before writing a session-log narrative, ADR context section, retrospective, or PR description. Four layers ordered by reader-trust, not ease of detection. Use when you say `prose self-check`, `audit my writing for AI tells`, `does this read as AI-written`, or before emitting any prose artifact. Do NOT use for code style (use style-enforcement) or to rewrite human-authored text.
license	MIT

Prose Self-Check

Run this on prose you are about to emit. It catches AI-vernacular tells before the artifact lands. It audits your OWN output, not human-authored text. It does not touch code style (that is style-enforcement).

The layers are ordered by reader-trust, not by ease of detection. The cheap keyword signal (Layer 1) and the real reader-cited signal (Layers 2-4) point in different directions. Weight structural and semantic findings above lexical.

Empirical ranking from a public 89k-post study (John Carter, "I pulled ~90,000 Reddit posts about what makes writing sound like AI (Part 2)," r/ClaudeCode, 2026-06-22): em-dash 7.1% of reader cites, flat rhythm 4.0%, "not X, it's Y" 2.8%. Top keyword matches (however, moreover, nuanced) are ~0% reader-cited.

Triggers

• prose self-check
• audit my writing for AI tells
• does this read as AI-written
• check this prose before I send it

When To Use

Run before emitting any prose artifact: session-log narrative, ADR context section, retrospective, PR body, issue body, design doc, agent-authored comment.

Skip for: code (use style-enforcement), pure data/config files, human-authored text you were asked to preserve, and one-line acknowledgements with no prose.

Process

Run the four layers in order. Do not stop at Layer 1; the weakest signal is first so you clear it cheaply, then spend attention where readers actually look.

Layer 1: Lexical (weakest signal)

Tiered banned-list check. The canonical word list is NOT duplicated here. It lives in one place: the "Banned Vocabulary" section of .claude/rules/voice.md. Read that section now and apply it. Do not maintain a second copy; a forked list drifts.

Two tiers:

• High-signal (scrub on sight). Em-dash (U+2014) and en-dash (U+2013), any leftover model-identity phrase ("as an AI language model", "I'm just an AI"), and the strongest tells in the voice.md list (for example delve, tapestry, showcase). Remove these whenever they appear.
• Low-signal (do NOT flag on presence alone). Words that top keyword scans but readers rarely cite: however, thus, moreover, additionally, nuanced, comprehensive. Flag one of these ONLY when the sentence it sits in is also empty or bland (fails Layer 4). A blanket scrub here produces the "robot pretending not to be a robot" over-correction and reads worse.

The em-dash/en-dash ban is also a hard repo rule (.claude/rules/universal.md MUST NOT 5). Verify at the byte level, not by eye:

python3 -c 'import sys; print(open(sys.argv[1],"rb").read().count(chr(0x2014).encode()))' FILE

Swap 0x2014 (em-dash) for 0x2013 (en-dash) to count en-dashes. Both counts must be 0 outside the tests/hooks/fixtures/ carve-out.

Layer 2: Structural

Sentence- and paragraph-shape tells. These are the #1 reader-cited sentence-level signals and survive any keyword pass. Scan for and rewrite:

• Contrast framing: not X, it's Y / it's not just X, it's Y / X isn't about Y, it's about Z. The single most-cited sentence tell. Rewrite to state the claim directly.
• Manufactured trailing offers: a closing sentence that proposes new, uninvited scope ("Want me to also...", "I could also...", "Let me know if you'd like..."). Delete it. (Mirrors the STOP-TOKEN rule in CLAUDE.md.)
• Rule-of-three padding: three parallel adjectives or clauses where one carries the meaning ("fast, reliable, and scalable"). Cut to the load-bearing term.
• Signposting / throat-clearing openers: Honestly, / Look, / Let's dive in / It's worth noting that / In today's landscape. Delete the opener; lead with the point.
• Inline-header lists: bullets that each start with a bolded restatement of the bullet ("Speed: it is fast.") when the bold adds nothing. Drop the bold label or fold into prose.

Layer 3: Distributional (proxy only)

Two reader-cited tells invisible to keyword passes. Use the proxies below; a classifier (Pangram, GPTZero) is OUT of scope because it cannot run in-agent.

• Burstiness proxy (flat rhythm, #2 reader cite): AI prose clusters near a uniform sentence length. Human prose varies. Compute the spread of sentence lengths in the artifact; if they are all within a narrow band, break some up and run others together. Use the helper:

python3 .claude/skills/prose-self-check/scripts/burstiness.py FILE

It prints sentence count, mean length, standard deviation, and a flat-rhythm warning when variance is low. The warning is a prompt to vary rhythm, not a hard gate.

• Concreteness proxy: count named entities, numbers, and file paths. Prose with near-zero of these is usually saying nothing (feeds Layer 4). The same helper reports a concreteness count.

Layer 4: Semantic (emptiness gate)

The top-5 tell no scanner sees: fluent text that says nothing. For each paragraph, name the one disagreeable claim it makes, the thing a reasonable reader could push back on. If you cannot name it, the paragraph is filler. Either give it a real claim with evidence or cut it.

This is where low-signal Layer 1 words get adjudicated: a however inside a paragraph that survives the emptiness gate stays; a however inside filler goes with the filler.

Output

After running all four layers, the artifact should:

• Carry zero em-dashes and en-dashes (byte-verified).
• Carry no high-signal lexical tells.
• Contain no not X, it's Y contrast framing and no manufactured trailing offer.
• Vary sentence length (no flat-rhythm warning, or a deliberate reason to keep it).
• Have every paragraph make a nameable, disagreeable claim.

Report what you changed, layer by layer, so the next reader can audit the pass.

Anti-Patterns

• Stopping at Layer 1 because the keywords are clean. The reader signal is in Layers 2-4.
• Reflexively scrubbing every however and moreover. Over-correction reads as AI overcompensating. Flag low-signal words only when Layer 4 also fails.
• Copying the banned-word list into this skill. The list lives in .claude/rules/voice.md; read it there.
• Running this on human-authored text. This is self-check on agent output only.
• Treating the burstiness warning as a hard gate. It is a proxy and a prompt.

Scripts

scripts/burstiness.py

Layer 3 helper. Computes sentence-length variance (burstiness) and a concreteness count (numbers, file paths, multi-word capitalized entities) for a prose artifact. It is a proxy, not a gate.

Usage:

python3 .claude/skills/prose-self-check/scripts/burstiness.py FILE
python3 .claude/skills/prose-self-check/scripts/burstiness.py FILE --json

It prints sentence count, word count, mean and standard deviation of sentence length, coefficient of variation, concreteness count, and a flat-rhythm warning when variance is low and there are at least four sentences.

Exit codes (ADR-035):

• 0 analyzed successfully (with or without a flat-rhythm warning)
• 2 configuration or input error (missing file, unreadable path)

Verification

python3 .claude/skills/prose-self-check/scripts/burstiness.py FILE
echo "exit=$?"   # 0 = analyzed, 2 = bad input

• Exit 0 means the script analyzed the artifact; exit 2 is a bad-input error.
• Em-dash and en-dash byte counts are both 0 (Layer 1).
• No not X, it's Y contrast framing and no manufactured trailing offer (Layer 2).
• No flat-rhythm warning, or a deliberate reason to keep the rhythm (Layer 3).
• Every paragraph makes a nameable, disagreeable claim (Layer 4).

The gate is your own four-layer pass, not the script. The script supports Layer 3; Layers 1, 2, and 4 are judgment applied against voice.md and the patterns above.

Evidence

• John Carter, "I pulled ~90,000 Reddit posts about what makes writing sound like AI (Part 2)," r/ClaudeCode, 2026-06-22 (unslop-ai-text scanner + 600-post hand-audit). Establishes the cited-vs-keyword divergence.
• Wikipedia: Signs of AI Writing (WikiProject AI Cleanup), 29-pattern catalog.
• Kobak et al. 2025 (arXiv 2406.07016), excess-vocabulary; "delves" +6,697%.
• Juzek & Ward, COLING 2025, traces lexical overrepresentation to RLHF.

Related Skills

Skill	Relationship
style-enforcement	Code style; this skill is prose only
prompt-engineer	Authors prompts; does not audit emitted prose
doc-accuracy	Doc factual accuracy; orthogonal to vernacular

References

• Banned vocabulary source of truth: .claude/rules/voice.md ("Banned Vocabulary" section).
• Em-dash/en-dash MUST NOT: .claude/rules/universal.md.
• Skill standards: .claude/skills/CLAUDE.md.