sepia is a free, open source ai development platforms project written in Python and released under MIT. It has 3,584 GitHub stars, 219 forks and 8 open issues, and was last pushed 8 hours ago. On this registry it ranks #131 of 155 tracked projects in AI Development Platforms, with 5 head-to-head comparisons available. It gained 666 stars over the last 30 tracked days.

What is sepia?

sepia is a de-AI writing skill that any Agent Skills-compatible agent can load — 77+ agents through the Skills CLI, with native plugin packaging for Claude Code, Codex, Grok Build, and Antigravity — and it is built for fiction writers and engineering teams who need prose to stop reading as machine-generated.

What it is

sepia is a portable Agent Skill built to the agentskills.io specification. It ships as one canonical SKILL.md with no per-platform forks, so any agent that speaks the Agent Skills standard can load it, while Claude Code, Codex, Grok Build, Antigravity, and QwenPaw additionally receive native plugin packaging. It is written in Python under the MIT licence, and the repository carries localized READMEs in English, Traditional Chinese, and Simplified Chinese. The skill covers two distinct bodies of writing: fiction, and professional prose such as release notes, PR and issue replies, postmortems, tickets and work orders, technical articles, and long-form journalism.

The concrete problem is that popular humanizers work on the wrong layer. They edit word choice and syntax. StoryScope (Russell et al., 2026, arXiv:2604.03136; 61,608 stories, human plus five frontier LLMs) showed that a classifier using narrative-structure features alone detects AI fiction at 93.2% macro-F1, and that in the study's LAMP-edited condition, where human editors had rewritten the surface style, detection fell only from 95.5% to 93.9%. The surviving tells are architectural: themes explained by the narrator, single-track causally tidy plots, emotion rendered only as bodily sensation, absent real-world references, no reader, linear time, endings resolved by protagonist growth and acceptance. sepia replaces the surface humanizer layer with a catalogue of those measured structural gaps.

Key capabilities

  • Four operations on the same skill: write, review (diagnose only), refactor (minimal edits), and recreate (full rewrite).
  • A three-pass protocol for fiction: pass one repairs narrative architecture, pass two repairs discourse flow, and pass three handles surface style such as clichés, syntax templates, vocabulary, and register.
  • A 30-feature diagnosis rubric plus per-model fingerprints in two layers: narrative tells measured by StoryScope for Claude, GPT, Gemini, DeepSeek, and Kimi, and sentence-level tells taken from the vendors' own prompting guides for Claude Fable 5.1 and Mythos 5.1, Fable 5 and Mythos 5, Opus 5, Opus 4.8, GPT-5.6, GPT-6 Astra, and the Gemini 3 series.
  • Per-venue rule files layered on one shared checklist for release notes and announcements, PR and issue replies, postmortems, tickets and work orders, technical articles, and long-form journalism.
  • Rules matched to the document type, such as answering first and citing file:line in PR replies, or requiring timestamps, dead ends, and owned action items in postmortems.
  • Continuous behavioural evaluation through the behavioral-eval.yml workflow, alongside a version-consistency.yml check.

Who uses it and how

  • Fiction writers who revise existing drafts and need the narrative architecture repaired before anyone touches word choice.
  • Engineering teams producing release notes, PR and issue replies, postmortems, and tickets, where the venue rules differ per document type.
  • Technical writers and journalists working on technical articles and long-form journalism, including features, investigations, and data stories.
  • Users of agents with a known generation model, where the matching per-model fingerprint layer is applied rather than a generic profile.
  • Multi-agent setups, since installation runs through the Skills CLI and its 77+ supported agents, with native plugins where available.

Getting started

Install through the Skills CLI at skills.sh, which supports 77+ agents and installs the skill with one command; Claude Code, Codex, Grok Build, Antigravity, and QwenPaw also get native plugin packaging. Any other agent that implements the agentskills.io specification can load the canonical SKILL.md directly.

How it compares

The facts provided name no comparable tool in this registry, so sepia stands alone here; the category it argues against is the popular humanizer, which edits word choice and syntax rather than narrative structure. Its position rests on the measured finding that surface rewriting leaves the detectable tells largely intact while structural features carry them.

When to use it — and when not to

A user must already have an Agent Skills-compatible agent, or run the Skills CLI, because sepia is a skill rather than a hosted service; the README excerpt provided names no Docker image, compose file, or hosted option. Anyone wanting a stand-alone writing application or a plain word-level humanizer should look elsewhere, since the first two passes deliberately work above the sentence. The excerpt supplied here is also truncated mid-table, so a prospective user should read the full repository before assuming coverage of every document type.

project readme (upstream, from github) — read inline

sepia

English | 繁體中文 | 简体中文

behavioral eval version consistency release license: MIT

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).

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

Plus a 30-feature diagnosis rubric and per-model fingerprints in two layers: narrative tells measured by StoryScope (Claude, GPT, Gemini, DeepSeek, Kimi) and sentence-level tells taken from the vendors' own prompting guides (Claude Fable 5.1 and Mythos 5.1, Fable 5 and Mythos 5, Opus 5, Opus 4.8; GPT-5.6, GPT-6 Astra; Gemini 3 series), applied when the writing or executing model is known. Vendors that publish no such guidance are recorded as consulted, not guessed.

Professional prose fails differently. 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

The governing principle throughout: 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. The operation wrappers depend on their sibling canonical skill, so standalone wrapper installation is unsupported; install the complete plugin package. What was verified on each platform is stated under Install.

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 contract in short: sepia's architecture decisions come first. The voice's moves are applied selectively (3–5 signature moves per piece, formula endings deliberately broken sometimes). Review reports the voice's known costs instead of fixing them away, while uniformity findings keep full strength: a voice does not excuse a metronome. On professional routes the venue still sets the register. Direct conflicts come back to you. The interface is grounded in one blind review experiment on a strict-minimalism specimen — a worked example, not measured evidence. One built-in profile ships under references/voices/ (Hemingway: iceberg omission for fiction, the Kansas City Star rules for professional prose, each move traced to its source). The built-in profile follows the same rule. On fiction, a review only reports when your text's recorded findings fit the Hemingway profile; it loads nothing. Asking for strong de-AI on a story counts as opting in, and sepia then says which profile it is applying and how to decline. /sepia-hemingway is the direct entry.

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); mean sentence length, punctuation counts, and paragraph length are not treated as signals because the measured directions contradict each other. Chinese text loads references/languages/zh.md, a calibration built on the one human-vs-machine Chinese corpus (HC3, 2023) plus a private human-side measurement of Traditional Chinese journalism (about two thousand articles from one unnamed Taiwanese publication over about ten years; corpus not distributed), with the limits of both stated in the file; evidence and numbers are in research/rhythm-syntax.md, and the journalism digest is research/zh-news-corpus.md.

Install

Every command below is written for user scope — install once, use it in every project.

Any agent (Skills CLI, 77+ agents)

npx skills add Nanako0129/sepia -g     # -g = user scope; the default is project
npx skills update sepia -g             # update
npx skills remove sepia -g             # uninstall

Installs on every agent the Skills CLI supports — Cursor, Cline, Windsurf, Copilot, OpenCode, goose, and more. Pick your agents when prompted. Runtime behavior outside the five platforms below has not been exercised by us; the skill is plain markdown under the Agent Skills standard, so file an issue if your agent trips on it.

The five platforms below have native plugin installers, each exercised with a live install (QwenPaw's by its contributor, see that section). Verified means the install completes and the sepia entries appear. Whether the entries then behave as documented has not been checked platform by platform.

Claude Code

# install
claude plugin marketplace add Nanako0129/sepia
claude plugin install sepia@sepia --scope user

# update
claude plugin marketplace update sepia
claude plugin update sepia

The in-session /plugin install dialog asks you to pick a scope — choose User there.

Codex

# install
codex plugin marketplace add Nanako0129/sepia
codex plugin add sepia@sepia

# update — refresh the marketplace snapshot, then re-add to pick up the new version
codex plugin marketplace upgrade sepia
codex plugin add sepia@sepia

Grok Build

# install
grok plugin install Nanako0129/sepia --trust

# update
grok plugin update

Grok also auto-discovers a Claude Code install of sepia if you have one; either route works.

Antigravity

# install directly from GitHub
agy plugin install https://github.com/Nanako0129/sepia

QwenPaw

# install: qwenpaw takes a local directory (or a zip URL), so clone first;
# the package's skills symlink resolves inside the clone
git clone https://github.com/Nanako0129/sepia
qwenpaw plugin install ./sepia/.qwenpaw-plugin

# uninstall
qwenpaw plugin uninstall sepia

Contributor-verified on QwenPaw 2.2.1 (#250, not reproduced by the maintainer): the install completes and /sepia is routed, with the packaged skills symlink followed into a real tree by shutil.copytree.

Project scope (alternative)

When one repo should pin its own copy, commit skills/sepia/ into that repo as .agents/skills/sepia (Codex + Antigravity) or .claude/skills/sepia (Claude Code).

Uninstall

Each tool uses its native command:

# Claude Code
claude plugin uninstall sepia@sepia --scope user

# Codex
codex plugin remove sepia@sepia

# Grok Build
grok plugin uninstall sepia

# Antigravity
agy plugin uninstall sepia

# QwenPaw
qwenpaw plugin uninstall sepia

Layout

sepia/
├── plugin.json              # Antigravity packaging
├── skills/
│   ├── sepia/                # canonical skill (Agent Skills standard)
│   │   ├── SKILL.md          # routing, operations, calibration rules, guardrails
│   │   └── references/       # passes, rubric, fingerprints, domain rules, languages/zh.md, voice-skills (experimental)
│   ├── sepia-write/SKILL.md  # thin fixed-operation wrappers
│   ├── sepia-review/SKILL.md
│   ├── sepia-refactor/SKILL.md
│   ├── sepia-recreate/SKILL.md
│   └── sepia-hemingway/SKILL.md  # fiction write/refactor with the built-in voice
├── .claude-plugin/          # Claude Code packaging (plugin.json, marketplace.json)
├── .codex-plugin/           # Codex packaging
├── .qwenpaw-plugin/         # QwenPaw packaging (plugin.json, plugin.py, skills symlink)
├── .agents/                 # Codex/Antigravity workspace-mode discovery + Antigravity workflow
└── research/                # digested evidence base with sources

Star History

Star History Chart

Sources

Full digests with links in research/. Primary: StoryScope (arXiv:2604.03136); LAMP (CHI 2025); Measuring AI Slop (arXiv:2509.19163); Reinhart et al. (PNAS 2025); Russell et al. (ACL 2025); NarraBench (arXiv:2510.09869); Echoes in AI (PNAS 2025); QUDsim (COLM 2025); Beguš (2024); Beyond Checkmate (EMNLP 2025); Nonaka & Perry (2025); Chakrabarty et al. (2026); Shan, Lee & Hao (2026); Rohrbacher et al. (2026); Sourati et al. (2026).

Support

You can use sepia for free without an account. The research behind every rule is open. Ongoing costs are just maintainer time and two kinds of model quota: delegating literature surveys to research agents that read primary papers, and running live models to test rule changes on A/B stories and cross-platform end-to-end reviews before shipping. You can support the project on Patreon.

Support sepia on Patreon

License

MIT

Frequently asked questions

Is sepia free to use?

sepia is open source under the MIT licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does sepia do?

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. Nar

What is sepia written in?

sepia is primarily written in Python. Its source is publicly available at https://github.com/Nanako0129/sepia, and it has 3,584 GitHub stars.