# Prose Self Check > 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. - Skill: `rjmurillo/prose-self-check` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add rjmurillo/prose-self-check` - Raw SKILL.md: https://api.skillmd.com/api/skills/rjmurillo/prose-self-check/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: rjmurillo (https://skillmd.com/u/rjmurillo) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/rjmurillo/prose-self-check --- # 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 Layers 1 to 3 are pattern matches over text, so scripts run them. Layer 4 is the one no scanner can do, and it is where your attention belongs. Start by running both helpers over the artifact: ```bash python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" FILE python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/burstiness.py" FILE ``` `prose_lint.py` covers Layers 1 and 2 and exits 1 on any high-severity finding. Fix what it reports, re-run until it exits 0, then do Layer 4 by hand. Do not scan for these tells by eye; you will miss some and spend attention you need for Layer 4. ### Layer 1: Lexical (weakest signal) `prose_lint.py` runs this layer. It reports two tiers, and the tiering is the point: - **High-signal (`high`, fails the run).** Em-dash (U+2014) and en-dash (U+2013), model-identity phrases (`as an AI language model`), and the strongest tells in the banned list. Remove every one. - **Low-signal (`info`, never fails).** Words that top keyword scans but readers rarely cite. The script reports them so Layer 4 can adjudicate: a flagged word inside a paragraph that makes a real claim stays; one inside filler goes with the filler. A blanket scrub here produces the "robot pretending not to be a robot" over-correction and reads worse. The exact low-signal set is the `LOW_SIGNAL_WORDS` constant in the script. The canonical word list is NOT duplicated in this skill or in the script. It lives in one place, the "Banned Vocabulary" section of `.claude/rules/voice.md`, and `prose_lint.py` parses it from there at runtime. A forked list drifts. The dash ban is also a hard repo rule (`.claude/rules/universal.md` MUST NOT 5). The script verifies it at the character level, so no eyeballing is needed. ### Layer 2: Structural Sentence- and paragraph-shape tells. These are the #1 reader-cited sentence-level signals and survive any keyword pass. `prose_lint.py` detects the three that have a fixed shape: - **Contrast framing** (`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** (`trailing_offer`): a sentence proposing 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.) - **Signposting / throat-clearing openers** (`signposting`): `Honestly,` / `Look,` / `Let's dive in` / `It's worth noting that` / `In today's landscape`. Delete the opener; lead with the point. Two shapes stay yours because judging them needs the meaning, not the string: - **Rule-of-three padding**: three parallel adjectives or clauses where one carries the meaning ("fast, reliable, and scalable"). Cut to the load-bearing term. Whether the third term is padding or content is a reading, not a match. - **Inline-header lists**: bullets that each open with a bolded restatement ("**Speed**: it is fast.") when the bold adds nothing. Drop the label or fold into prose. The same shape is correct when the label is a real index into a set, which is why the script does not flag it. ### 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: ```bash python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.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 `comprehensive` inside a paragraph that survives the emptiness gate stays; one inside filler goes with the filler. The tiers are intersected with the voice rule, so a low-signal word the rule does not ban (`however`, `thus` today) stays dormant and never reaches you. ## Output After running all four layers, the artifact should: - Exit 0 from `prose_lint.py`, which means zero dashes, no high-signal lexical tells, no contrast framing, no manufactured trailing offer, and no signposting opener. - Have every `info` finding adjudicated by Layer 4 rather than scrubbed. - 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 - Scanning for these tells by eye. `prose_lint.py` does Layers 1 and 2 exactly; hand-scanning is slower, misses matches, and spends the attention Layer 4 needs. - Stopping when `prose_lint.py` exits 0. It clears Layers 1 and 2 only. A clean exit says nothing about whether the prose says anything. - Reflexively scrubbing every `info` finding. Over-correction reads as AI overcompensating. Cut a low-signal word only when Layer 4 also fails. - Copying the banned-word list into this skill or into the script. The list lives in `.claude/rules/voice.md`; the script parses it from 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 ### prose_lint.py Layers 1 and 2. Reports dashes, banned vocabulary (parsed from the voice rule), contrast framing, trailing offers, signposting openers, and model-identity phrases. Fenced code blocks and inline code spans are skipped. Usage: ```bash python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" FILE [FILE ...] python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" - < draft.md python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" FILE --json python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" FILE --rules PATH ``` Each finding prints as `FILE:LINE:COLUMN: SEVERITY: KIND: MATCH (note)`. `-` reads stdin, which is how you check a draft that is not a file yet. Every run ends with what it examined, not only what it found: `0 findings in N prose line(s) of M in K file(s)`. A fence that never closes hides the rest of the document from the scanner, so it is reported as its own high-severity `unterminated_fence` finding rather than letting a barely-read file exit 0. Layer 2 matches across the whole document, so a tell that straddles a hard wrap is still caught. A blank line ends the match: the shapes are sentences, not paragraphs. The voice rule is discovered in this order: `--rules`, then `$CLAUDE_PLUGIN_ROOT/rules/voice.md`, then `$COPILOT_PLUGIN_ROOT/instructions/voice.instructions.md`, then `.claude/rules/voice.md` or `.github/instructions/voice.instructions.md` under the current directory, then `rules/voice.md` or `instructions/voice.instructions.md` under the plugin install root (the directory holding `.claude-plugin/plugin.json`). When no copy is reachable the script warns on stderr and runs the dash and structural checks only, so a vendored install degrades instead of failing. Exit codes (ADR-035): - `0` no high-severity findings (`info` findings may still be present) - `1` at least one high-severity finding - `2` configuration error (a named file or the rules file cannot be read) ### 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: ```bash python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/burstiness.py" FILE python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.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 ```bash python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/prose_lint.py" FILE echo "exit=$?" # 0 = Layers 1-2 clean, 1 = findings, 2 = bad input python3 "${COPILOT_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.claude}}/skills/prose-self-check/scripts/burstiness.py" FILE echo "exit=$?" # 0 = analyzed, 2 = bad input ``` - [ ] `prose_lint.py` exits 0 (Layers 1 and 2 clean). - [ ] Every `info` finding was adjudicated by Layer 4, not scrubbed on sight. - [ ] 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](../style-enforcement/SKILL.md) | Code style; this skill is prose only | | [prompt-engineer](../prompt-engineer/SKILL.md) | Authors prompts; does not audit emitted prose | | [doc-accuracy](../doc-accuracy/SKILL.md) | 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`.