Sepia
De-AI writing skill for any Agent Skills-compatible agent (77+ via the Skills CLI), with native plugins for Claude Code, Codex, Grok Build, and Antigravity.…
Install
npx skills add Nanako0129/sepia -gclaude mcp add sepia -- npx skills add Nanako0129/sepia -g{
"mcpServers": {
"sepia": {
"command": "npx",
"args": [
"skills",
"add",
"Nanako0129/sepia",
"-g"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →sepia
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
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.
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