sepia

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De-AI writing at the layer that actually gives AI away. Fiction gets its narrative architecture repaired before anyone touches word choice; professional documents (release notes, PR replies, postmortems, tickets, technical articles) each get rules matched to their venue.

A portable Agent Skill: any agent that speaks the standard can load it, and the Skills CLI, which supports 77+ agents, installs it with one command. Claude Code, Codex, Grok Build, Antigravity, and QwenPaw additionally get native plugin packaging. One canonical SKILL.md, no per-platform forks. Four operations: write, review (diagnose only), refactor (minimal edits), recreate (full rewrite).

Table of Contents

• Why another humanizer
• Operation entries
• Experimental: composing with voice skills
• Sentence rhythm and Chinese calibration
• Install
• Uninstall
• Layout
• Star History
• Sources
• Support
• License

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Why another humanizer

Every popular humanizer edits word choice and syntax. StoryScope (Russell et al., 2026: 61,608 stories, human + 5 frontier LLMs) showed that a classifier using narrative-structure features alone detects AI fiction at 93.2% macro-F1. In the same study's LAMP-edited condition, where human editors had rewritten the surface style, detection dropped only from 95.5% to 93.9%. The tells that survive are architectural: themes explained by the narrator, single-track causally-tidy plots, emotions rendered only as bodily sensation, no real-world references, no reader, linear time, endings resolved by protagonist growth and acceptance.

sepia turns those measured gaps, together with the related studies digested in research/, into a three-pass writing and revision protocol for fiction:

Pass	Layer	Examples
1	Narrative architecture (fiction)	stop explaining the theme, loosen the causal chain, back-load revelations, mix emotion modes, sparse character networks, name real things
2	Discourse flow	de-template the paragraph-question sequence, fix the mid-story sag, vary rhythm and positions
3	Surface style	the classic layer: clichés, syntax templates, vocabulary, register

A 30-feature diagnosis rubric and per-model fingerprints across two layers apply when the writing or executing model is known:

Model family	Narrative layer (StoryScope)	Sentence-level prose layer (Vendor prompting guides)
Claude	Measured	Claude Fable 5.1 and Mythos 5.1, Fable 5 and Mythos 5, Opus 5, Opus 4.8
GPT	Measured	GPT-5.6, GPT-6 Astra
Gemini	Measured	Gemini 3 and 3.1
DeepSeek	Measured	Consulted (no guidance published)
Kimi	Measured	Consulted (no guidance published)

Notice: Vendors that publish no prompt guidance are recorded as consulted, not guessed.

Professional prose fails differently, and the structure-level finding holds there too: a 2026 replication of StoryScope on 2,250 company blog posts against 11,250 AI mirrors separated them at 98.0 macro-F1 from structural features alone, with the AI shape described as tidy and self-announcing (SLOPSHAPE-2026 in the ledger, arXiv:2609.15369; a preprint whose features are LLM-scored; it tested detection of original and model-self-reworded posts, never human editing). The studies digested in research/ point at filler that carries no information, hedging where a judgment was needed, chatbot leftovers, register that ignores the venue, and formatting that looks stamped out. Each document type gets a thin rule file on top of one shared checklist:

Domain	The gist
Release notes / announcements	user impact first, artifacts per claim, no marketing inflation
PR / issue replies	answer first, cite file:line, no reflex praise, length ∝ stakes
Postmortems	blameless toward people, merciless toward mechanisms; timestamps, dead ends, owned action items
Tickets / work orders	title = outcome, testable acceptance criteria, link don't repeat
Technical articles	open at the problem, one real dead end, one committed opinion, numbers with conditions
Long-form journalism (features, investigations, data stories)	lead and body in two registers, quotations keep their spoken texture, every number carries a comparison, no summary ending

Governing principle: Calibrate to the human distribution, don't invert the AI one. Humans sit at moderate values; a story with every rule applied is a new fingerprint. The skill selects 3–5 moves per story and leaves slack.

Operation entries

The complete plugin package gives Claude Code, Codex, Grok Build, and Antigravity a general router plus five direct entries. QwenPaw gets the /sepia router only, so the table below does not apply there:

Operation	Claude Code	Codex	Grok Build	Antigravity	Meaning
write	/sepia-write	$sepia-write	/sepia-write	/sepia-write	Create new prose
review	/sepia-review	$sepia-review	/sepia-review	/sepia-review	Diagnose without editing
refactor	/sepia-refactor	$sepia-refactor	/sepia-refactor	/sepia-refactor	Make minimal in-place edits
recreate	/sepia-recreate	$sepia-recreate	/sepia-recreate	/sepia-recreate	Rewrite from the source facts and intent
hemingway	/sepia-hemingway	$sepia-hemingway	/sepia-hemingway	/sepia-hemingway	Write or refactor fiction with the built-in Hemingway voice applied

The general /sepia (Claude Code, Grok Build, Antigravity, and QwenPaw) or `$sep