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.

1. 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.

1. 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.

1. 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.

1. Never modify the manuscript. This skill emits a markdown

report only.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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]].

1. Rank within each dimension. Sort flagged sentences by

distance from the threshold, most-extreme first.

1. 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.

1. 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:

# 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.