sepia

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 $sepia (Codex) router remains available; on QwenPaw the package installs the six skills into each workspace and registers no per-operation slash commands. What was verified on each platform is stated under Install.

Notice: Standalone wrapper installation is unsupported. The operation wrappers depend on their sibling canonical skill; install the complete plugin package.

Experimental: composing with voice skills

Since v0.4.0, sepia defines an interface for stacking a voice or style skill on top of it — a minimalism method, a brand voice, a persona guide. It is opt-in: tell sepia the voice skill is in play, and it loads references/voice-skills.md over the normal route. No external voice is loaded unless you say so.

The interface contract operates under fixed precedence rules across operations and routes:

Rule dimension	Contract specification
Architecture	sepia's architecture decisions come first.
Move selection	Voice moves are applied selectively (3–5 signature moves per piece, fewer when a sparse shape or the facts offer fewer; formula endings deliberately broken sometimes).
Review diagnostics	Review reports the voice's known costs instead of fixing them away.
Uniformity enforcement	Uniformity findings keep full strength: a voice does not excuse a metronome.
Professional register	On professional routes, the venue still sets the register.
Conflict resolution	Direct conflicts come back to you.

Notice: The voice-skills interface is grounded in one blind review experiment on a strict-minimalism specimen — a worked example, not measured evidence.

Two built-in profiles ship under references/voices/, alongside a persona profile kind with declared override rights:

Profile	Target routes	Source & characteristics	Opt-in & invocation
Hemingway (references/voices/hemingway.md)	Fiction and professional prose	Iceberg omission for fiction, the Kansas City Star rules for professional prose; each move traced to its source	Direct entry /sepia-hemingway (or $sepia-hemingway). On fiction, asking for strong de-AI on a story counts as opting in (sepia announces the profile and how to decline). Without an opt-in, a fiction review only reports when the text fits the profile and loads nothing.
Taiwan long-form journalism (references/voices/tw-journalism.md)	Professional routes only	Nine narrative shapes with their moves, drawn from private human-side journalism measurement and its close reading	Opt-in phrase: "apply the Taiwan journalism voice". Follows the same rules as Hemingway.
Persona template (references/voices/PERSONA-TEMPLATE.md)	Author-defined	One writer's style with declared override rights; checked by scripts/check_persona.py	Author-created profile. Template at references/voices/PERSONA-TEMPLATE.md.
Nyaneko (references/voices/personas/nyaneko.md)	Professional routes only	Built-in companion voice written from the maintainer's voice specification with her own exemplars	Opt-in phrase: "apply persona Nyaneko" or 「套用 persona Nyaneko」.

Writing with a persona: a short how-to

sepia's rules remove tells. Warmth has another source: the writer knowing who is speaking, and to whom. One case from this project's own release, with nothing measured: the v0.12.0 Threads post was drafted twice by the same model from the same release notes. The first prompt handed it a skeleton (version line, bullets, fixes, engineering note, update line) and got a form filled in. The second prompt handed it an identity, "I maintain this project and just shipped v0.12.0; talk to my followers in my own voice; how you organise it is yours", plus the notes as the only fact source and the platform's limits, and got the post the maintainer kept. A sepia review of that post then found its tells clustered at the opening and the close (an opening about how the change came to be, a maxim ending, a few absolutes), and a refactor took them out without touching the middle.

The order that worked:

1. Identity first.
2. Facts separately, as the only source.
3. Constraints stated as platform limits, with the structure left to the model.
4. sepia as the check.

Do not use tools; everything you need is below.
I am <who>, writing for <whom>, about <what just happened>. Use my own voice; how to organise it is yours.
Platform limits: <length, markup, mentions>.
Facts come only from the notes below; numbers and identifiers must match them.
===== notes =====
<the source document>

A persona profile is that identity written down once so every piece starts from it: what they are to the reader, what their first sentence does, how warmth attaches to a fact, what they never do, and pieces in their own voice as exemplars. The template is references/voices/PERSONA-TEMPLATE.md, the built-in references/voices/personas/nyaneko.md is a worked example, and the interface validator checks the profile structure:

python3 scripts/check_persona.py <file>

Write your own; the exemplars carry more than the description does.

Sentence rhythm and Chinese calibration

The style pass checks the spread of sentence lengths, the one syntactic measure on which every study that measured it agrees: human text varies more within a passage, in English and in Chinese.

| Syntactic measure | Status | Empirical basis |