| 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-checkaudit my writing for AI tellsdoes this read as AI-writtencheck 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=$?"
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
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.
