sepia

Original：🇺🇸 English

Translated

Make AI-generated writing read as human-written, in fiction and in professional prose. Repairs the narrative architecture of fiction and stories (based on StoryScope, arXiv:2604.03136); routes professional text through domain rules for release notes, announcements, PR and issue replies, code-review comments, incident postmortems, tickets, work orders, technical articles, and blog posts. Four operations - write, review (diagnose AI tells without editing), refactor (minimal in-place edits), recreate (full rewrite). Use when asked to humanize, de-AI, unslop, or strip AI flavor from any text; when writing or revising any of these document types; or whenever output must not read as machine-written.

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Sourcenanako0129/sepia

Added on2026-08-30

NPX Install

npx skill4agent add nanako0129/sepia sepia

SKILL.md Content

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Sepia — de-AI writing

Every rule here is backed by a measured human-vs-AI gap. The load-bearing facts: in fiction, a classifier using only narrative-structure features detects AI at 93.2% macro-F1 and style editing barely moves it — so structure is fixed before style, always. In professional prose, the measured tells are different — filler density, missing stance, chatbot residue, register mismatch, format uniformity — and the fix is domain-specific. Route first, then operate.

Routing

Text type	Load, in order
Fiction / stories / narrative essays	`references/narrative-pass.md` → `references/discourse-pass.md` → `references/style-pass.md` ; diagnose with `references/rubric.md`
Release notes, changelogs, announcements	`references/professional-pass.md` + `references/domains/release-notes.md`
PR replies, issue replies, review comments	`references/professional-pass.md` + `references/domains/dev-replies.md`
Incident postmortems / RCA	`references/professional-pass.md` + `references/domains/postmortems.md`
Tickets, work orders, bug reports	`references/professional-pass.md` + `references/domains/tickets.md`
Technical articles, blog posts, tutorials	`references/professional-pass.md` + `references/domains/tech-articles.md` + `references/discourse-pass.md` §1–3
Any other prose	`references/professional-pass.md` + `references/style-pass.md`

Every non-fiction route ends with the vocabulary/syntax scan in

references/style-pass.md

§2–3, and long professional pieces take the whole style pass — in both cases skipping its fiction-slop table. If the text was produced by a known model, add

references/model-fingerprints.md

(fiction-centric; use as priors).

Operations

Any request maps to one of four operations:

Operation	Contract
write	New content. Read the domain file before drafting — architecture and register decisions come first, they cannot be retrofitted cheaply. For fiction, follow Workflow A below.
review	Diagnose only — no edits. Produce the defect list (fiction: rubric report; professional: checklist findings with quoted evidence) and stop. Report findings; apply nothing until asked.
refactor	Minimal in-place revision preserving structure, voice, and intent. Two-stage: full defect list first, then fix item by item, deepest layer first. Skew replace/delete over insert (measured editor ratio 74/18/8).
recreate	Full rewrite. Extract the facts, claims, and intent from the original into a bare list; verify nothing invented; write fresh under the domain rules. Use when defects are structural and the text is short enough that surgery costs more than rebuilding.

The two-stage protocol is not optional for refactor/recreate: paraphrasing without a defect list makes AI fingerprints more visible, not less (measured on expert detectors).

Fiction workflows

A — writing new fiction: (1) premise, genre, length — genre sets calibration targets; (2) fill the architecture sheet in

references/narrative-pass.md

; (3) select 3–5 human-leaning moves + one rarity move; (4) outline, run the outline/QUD checks in

references/discourse-pass.md

and the echo test in

references/narrative-pass.md

§2; (5) draft; (6) self-diagnose with

references/rubric.md

, one group at a time; (7) style pass last.

B — revising existing fiction: (1) diagnose completely first (rubric → discourse → style), no edits; (2) triage — architecture defects need scene-level surgery, tell the user how deep before cutting; (3) fix deepest first; (4) verify: re-run changed rubric groups, read key passages aloud, echo-test any added twist.

Calibration — the rule that governs all rules

Principle	Meaning
Aim at the band, not the opposite pole	Human values are moderate (chronological discontinuity 2.4/5, not 5). Inverting every AI tell creates a new fingerprint. In professional prose the equivalent: match the venue's register, don't overshoot into forced casualness — informality alone fools no trained reader.
Select, don't accumulate	Human writing is diverse. Fiction: 3–5 moves per story, chosen for the premise, varied across works. Professional: fix what the checklist actually flags, nothing more.
Leave slack	Ordinary sentences, an underdeveloped thought, a plain paragraph. Do not sand every surface.

Hard guardrails

Never invent specifics. Fiction: intertextual references, brands, places must be real and correct. Professional: versions, numbers, timestamps, benchmarks, quotes come from the actual change/incident/data — missing info means ask the user or leave an explicit TODO, never fill. Confident wrong facts are themselves a top-tier tell.
Deletion beats addition (74% replace / 18% delete / 8% insert). The only additive fix is real specificity.
Respect the author's voice and the venue's corpus. Extract habits from the user's samples or the venue's recent artifacts before editing; edit toward that profile. Do not remove a mannerism they actually use.
Dialogue quotes and quoted material are load-bearing — do not regularize them.
Check the whitelists (
```
references/style-pass.md
```
§7,
```
references/professional-pass.md
```
last section) before flagging: clean grammar, formal tone in formal venues, and conventional templates are not evidence of AI.