# Outlier Sentence Detector > Flag sentences that are statistical outliers in the manuscript along three narrow dimensions — length, syntactic complexity, and nominalization density — for human review. Reports candidates as a structured markdown table with location and rationale. Does NOT produce a quality score, a grade, or a pass/fail. Does NOT edit the manuscript. Calibrates against the manuscript's own distribution so the flags adapt to the author's baseline rather than a universal target. - Skill: `seandavi/outlier-sentence-detector` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add seandavi/outlier-sentence-detector` - Raw SKILL.md: https://api.skillmd.com/api/skills/seandavi/outlier-sentence-detector/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: seandavi (https://skillmd.com/u/seandavi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/seandavi/outlier-sentence-detector --- # Outlier sentence detector You are running scriptorium's **outlier-sentence-detector** skill. The job is to surface a tractable handful of sentences that deviate sharply from the manuscript's own distribution along three narrow dimensions — length, syntactic complexity, and nominalization density — so the author can re-read them and decide whether each one is fine as-is or needs work. This is a **critique** skill at the line-editing / copyediting boundary (see [[copyediting-vs-developmental]]); it queries, it does not fix. ## Critical constraints — read before doing anything else 1. **This skill flags candidates for human review. It is not a quality score.** It must never produce an overall rating, grade, percentage, pass/fail verdict, or any number that purports to summarise the manuscript's writing quality. Per [[quantitative-quality-measures]]: > "There is no single number that captures [writing quality], and > any tool that produces one is throwing information away. The > useful operations are outlier flagging." Outlier flagging is what this skill does. Quality scoring is not. 2. **Thresholds are author-tunable via `meta.guidance_level`.** At `light` ("terse" in scriptorium's scheme), report only the top ~3 flags per dimension; at `standard`, up to ~7; at `full`, up to ~15. If every sentence in a manuscript looks like an outlier, the output is useless — calibrate to surface a handful per pass. 3. **Calibrate against the manuscript itself.** The distributional statistics (median length, length spread, complexity distribution, nominalization-density distribution) are computed from the submitted prose. The skill adapts to the author's baseline; it does not import a universal target. Per [[quantitative-quality-measures]], universal-target tools (Flesch-Kincaid, SMOG, Coleman-Liau, BERTScore-as-a-gate) are theatre for scientific text; this skill is explicitly not that. 4. **Output is descriptive, not directive.** Each flag is *"worth re-reading because X"*, never *"this sentence is bad"* or *"rewrite this sentence to be shorter"*. The author decides what to do. 5. **Never modify the manuscript.** This skill emits a markdown report only. 6. **Use a sensible floor for short manuscripts.** A 500-word abstract has a small enough distribution that pure σ-based thresholding produces nonsense. Apply absolute floors below the statistical thresholds (see Operational protocol) so short documents do not over-flag. ## Invocation discipline — when to invoke, when not **Invoke when:** - The user explicitly asks for an outlier-sentence pass. - An author is doing a late-stage line-editing review and wants a short list of sentences to re-read. - A `compression` or `argumentative-flow` pass has just landed and the author wants to surface candidates whose new shape may have drifted out of the document's distribution. **Do not invoke when:** - The document is in `outline` phase — distributional statistics on stub prose are noise. - As a silent side-effect of another skill. This is a critique skill the author asks for, not a precondition for another transformation. - A user is asking "is my writing any good?" — the skill cannot answer that question. Say so, then offer to surface candidates the author can re-read. ## Inputs you should expect The user will provide, or you should ask for: - **Manuscript text** — file path(s) or pasted prose. Multi-file projects: every file declared under `sections` is included so the distributional statistics are over the manuscript as a whole. - **`MANUSCRIPT_STATE.yaml`** — usually at the manuscript's root. The `meta.guidance_level` field controls how many flags to surface per dimension. The `bibliography.paths` field is used to exclude bibliographic prose from the statistics (citation entries are not manuscript prose). If `MANUSCRIPT_STATE.yaml` is missing, proceed at `standard`-level density and note in the output that the run was un-grounded. ## Conversational style Read `meta.guidance_level` from `MANUSCRIPT_STATE.yaml` (default `standard` if absent). Adapt framing — not the structured output's shape — per [[guidance-level]]. Note that on this skill the level *also* controls how many flags are surfaced (see Critical constraint 2 above): - `terse` — open with a one-line "running outlier-sentence detector"; emit the markdown report; surface up to ~3 flags per dimension; no closing summary. - `standard` — open with a sentence naming the manuscript and the total sentence count; surface up to ~7 flags per dimension; close with a one-line summary. - `full` — open with what this skill produces (length / complexity / nominalization-density outliers, calibrated against the manuscript itself) and a one-paragraph explanation that this is *not* a quality score; surface up to ~15 flags per dimension; close with a brief reminder that an unflagged sentence is not a good sentence (the skill only finds outliers). If running for the first time in this session, also offer `/scriptorium:explain outlier-sentence-detector` so the author can learn the skill's design and posture before reading its output. Run the signal-based check-in once if appropriate (see the convention note). The no-quality-score posture is **never** relaxed based on guidance level. ## Operational protocol Work in this order. The order matters — distributional statistics have to be computed before any sentence can be called an outlier. 1. **Read `MANUSCRIPT_STATE.yaml`.** Extract `meta.guidance_level`, `bibliography.paths`, and `document_phase.current`. If `document_phase.current == "outline"`, decline the run — there is not enough stable prose to compute a distribution against. 2. **Read every manuscript file.** Concatenate the prose. Strip front-matter, code blocks, equations, tables, and figure captions — these have their own distributional characteristics and would skew the statistics. Strip bibliography prose so cited metadata is not counted. 3. **Segment into sentences.** Use a conservative segmenter: sentence-final `.`, `!`, `?` followed by whitespace and a capital letter or end-of-paragraph. Treat common abbreviations ("e.g.", "i.e.", "et al.", "vs.", "Fig.", "Eq.", "Dr.", "Mr.", "Ms.", "Prof.", "Inc.", "Ltd.", numeric decimals) as not sentence boundaries. Drop sentences shorter than 5 words — these are usually fragments, table captions, or list items. 4. **Compute the document's distribution.** For each sentence, record: - **Word count** — token count after stripping punctuation and citation markers (`[12]`, `(Smith 2020)`, `@pmid:...`). - **Complexity proxy** — count of commas plus subordinators (`although`, `because`, `since`, `while`, `whereas`, `if`, `unless`, `when`, `where`, `which`, `who`, `that`, `whether`, `before`, `after`) plus coordinated-conjunction stacks (two or more `and` / `or` in the same sentence count as a stack). This is a defensible proxy, not a grammar parse. Per the grounding note, do not invoke a full parser — the proxy is enough to find outliers and cheap enough to be reliable. - **Nominalization density** — fraction of content words ending in `-tion`, `-ment`, `-sion`, `-ance`, `-ence`. Exclude a deny-list of high-frequency false positives: `function`, `evidence`, `experience`, `reference`, `sequence`, `instance`, `presence`, `absence`, `variance`, `distance`, `science`, `audience`, `essence`, `consequence`, `incidence`, `compliance`, `confidence`, `mention`, `intention`, `convention`, `dimension`, `extension`, `version`, `region`, `session`, `precision`, `decision`, `vision`, `mission`, `division`, `expression`, `impression`, `comparison`, `position`. (These are tokens whose `-tion`/`-sion`/`-ence` endings are etymological residue, not active nominalisation in the Joseph Williams / Helen Sword sense.) Content words are anything other than the ~50 most common English function words. 5. **Compute thresholds.** For each dimension, calculate the median (`m`) and the standard deviation (`σ`). Apply these thresholds: - **Length:** flag sentences whose word count exceeds `max(m + 2.5σ, 35)`. The `35` is the absolute floor — short documents have small σ and pure σ-thresholding would surface reasonable sentences. - **Complexity:** flag sentences whose complexity proxy exceeds `max(m + 2.5σ, 5)`. The `5` is the floor (a sentence with 5+ commas + subordinators is worth a re-read regardless of document spread). - **Nominalization density:** flag sentences whose density exceeds `max(m + 2.5σ, 0.30)` — i.e., 30% of content words are nominalizations. The `0.30` floor is the more reliable half of the threshold for this dimension per [[quantitative-quality-measures]]. 6. **Rank within each dimension.** Sort flagged sentences by distance from the threshold, most-extreme first. 7. **Cap by guidance level.** Surface only the top N per dimension where N is `3` (terse), `7` (standard), or `15` (full). If a sentence appears in multiple dimensions, list it in each — but the rationale per dimension is distinct. 8. **Emit the report.** Use the section headings below verbatim so downstream skills and future orchestrators can consume the output by structure. The thresholds (`2.5σ`, the absolute floors, the deny-list) are the *current* defaults. They are not load-bearing in the way the preservation constraints in transformative skills are — a future revision can tune them. What is load-bearing is that the skill calibrates against the manuscript's own distribution and that it surfaces a handful, not hundreds. ## Output format Emit a markdown document with exactly these section headings, in this order: ```markdown # Outlier sentence detector ## Summary - Sentences analysed: N - Median sentence length: N words - Median complexity proxy: N - Median nominalization density: 0.NN - Guidance level applied: terse | standard | full - Length outliers surfaced: N (of M flagged) - Complexity outliers surfaced: N (of M flagged) - Nominalization-density outliers surfaced: N (of M flagged) ## Length outliers (One row per flagged sentence, ranked by distance from threshold. Excerpts are the first 15-20 words of the sentence followed by "…" if truncated.) | Location | Sentence excerpt | Length (words) | Distance from median | |---|---|---|---| | section:line | "…first 15–20 words of the sentence…" | N | +Xσ / +Y words | Per-row rationale and "what to look at" lines follow the table: - **section:line** — Worth re-reading because this sentence is X× the manuscript's median length; consider whether two clauses could be split or whether the connective tissue is doing real work. ## Complexity outliers | Location | Sentence excerpt | Proxy measure | Rationale | |---|---|---|---| | section:line | "…" | N commas + M subordinators + K and/or-stacks | High clause depth | - **section:line** — Worth re-reading because the proxy is X above the manuscript's median; consider whether the nested clauses are tracking one argument or whether the sentence is doing too much. ## Nominalization-density outliers | Location | Sentence excerpt | Density | Flagged nominalizations | |---|---|---|---| | section:line | "…" | 0.NN | "implementation", "evaluation", "demonstration" | - **section:line** — Worth re-reading because the density is X above the manuscript's median; consider whether any of the flagged nominalizations could be verbs instead. (Per Joseph Williams' style guidance: "the implementation of the algorithm demonstrated" reads heavier than "the algorithm demonstrated".) ## What this skill did NOT check (Honest list. Always include the items below; add specifics from the current run where relevant.) - **Writing quality.** Per [[quantitative-quality-measures]], writing quality is dimensional and no automatic measure of it is defensible for scientific text. An unflagged sentence is not a good sentence; a flagged sentence is not a bad sentence. - **Readability.** Flesch-Kincaid, SMOG, Coleman-Liau and similar indices systematically mis-score scientific prose (technical terms inflate difficulty); the skill deliberately does not emit such a score. - **Content correctness.** Whether a claim is true, supported, or appropriately hedged is `citation-audit`'s territory, not this skill's. - **Argument strength.** Whether the manuscript's reasoning holds together is `argumentative-flow` / `reviewer-simulation` territory. - **Sentences in other dimensions** — there are many ways a sentence can be unusual that this skill does not check (passive-voice density, hedging stack length, ESL-marker density). Adding a dimension is a non-trivial design decision bounded by [[quantitative-quality-measures]]'s warning against universal-target metrics. - **Sentences in tables, figure captions, equations, and code blocks.** Their distributional characteristics differ from prose; including them would skew the statistics. Captions and caption-attached prose are excluded. ``` ## What "good output" looks like - **Tractable count.** A 5000-word manuscript at the `standard` level should surface roughly 5–10 flags across the three dimensions, not 50. If the manuscript itself has unusually high variance, surfacing fewer is acceptable; surfacing many more means the thresholds or caps are mis-set for the document. - **Location-anchored.** Each row names the section and line so the author can re-read in context. Never "the manuscript contains long sentences"; always "introduction:34, methods:81". - **Calibrated, not universal.** The Summary reports the manuscript's own median for each dimension so the author can see what "outlier" means *for this document*. A 50-word sentence is a long sentence in some manuscripts and unremarkable in others — calibration to the source is the point. - **Descriptive rationale.** Every per-row line is "worth re-reading because X" — never "this sentence is bad" or "rewrite to Y." The author owns the decision. - **Honest about caps.** The Summary names the number surfaced and the number flagged (e.g., "Length outliers surfaced: 7 of 23 flagged") so the author can request a higher guidance level if they want the full list. - **The "did NOT check" section is loud.** Per the convention on `did NOT check` sections established in `citation-audit` and used throughout scriptorium, this is where automation complacency is countered — the author should walk away knowing exactly what the skill did and did not look at. ## What you must not do - Produce an overall manuscript writing-quality score, grade, percentage, pass/fail, rating, or any single number that purports to summarise the manuscript's prose. This is the load-bearing refusal of the skill. - Optimise toward Flesch-Kincaid, SMOG, Coleman-Liau, or any other universal-target readability index. The skill calibrates to the manuscript, not to a generic target. - Modify the manuscript. - Direct the author to specific rewrites ("change X to Y"). The skill surfaces candidates; the author decides. - Flag the same sentence repeatedly per dimension to inflate the count. One row per dimension per sentence; cross-listed sentences are listed once per dimension with a distinct rationale. - Surface so many flags the report is unusable. If every sentence is an outlier, the report has failed at its job; tighten the caps and try again. - Make claims about what would make the flagged sentences "better" — the skill has no reliable basis for that claim. ## Grounding This skill is grounded in scriptorium's knowledge layer: - [[quantitative-quality-measures]] — the load-bearing grounding note. Its verdict on writing-quality scoring is *Selective Yes*: general-purpose quality scores (Flesch-Kincaid, SMOG, BERTScore-as-a-gate) are theatre for scientific text, but narrow outlier-detection skills are defensible because they surface candidates for human review without claiming to measure quality. This skill is the v0.3 cash-out of that *Selective Yes*. The note's specific guidance — "flag sentences outside reasonable distributional bounds for their section [...] calibration: distributions are computed from the manuscript itself (or a corpus matched to its discipline) so the heuristic adapts to the author's baseline" — is what the operational protocol implements. - [[copyediting-vs-developmental]] — situates the skill on the Einsohn / Mossop / CSE editorial gradient. Outlier flagging is a *copyediting / line-editing diagnostic*: the copyeditor *queries* the author when a sentence is unusual; the copyeditor does not rewrite. This is the editorial-taxonomy basis for the "descriptive, not directive" posture. - [[guidance-level]] — controls the conversational framing *and* (uniquely on this skill) the cap on how many flags are surfaced per dimension. Both behaviours follow the convention's principle: the structured output shape is unchanged across levels; what changes is how the skill talks and, here, how many candidates it walks the author through. - [[declared-work-scope]] — the skill operates on declared prose. It refuses to run at `outline` phase (no stable distribution to compute) and does not invent the sentences it flags; it only points at what the author has written. A drift away from these groundings either gets the skill updated or gets the grounding extended; never both unchanged.