sepia
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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- MIT
- 最近更新
- 2026-11-12
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01 前言 Front Matter
- name
- sepia
- license
- MIT
- metadata
- { "version": "0.2.0" }
- description
- 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.
02 资源文件 12
- 参考文档agents/openai.yaml191 B
- 参考文档references/discourse-pass.md4.7 KB
- 参考文档references/domains/dev-replies.md2.5 KB
- 参考文档references/domains/postmortems.md2.5 KB
- 参考文档references/domains/release-notes.md2.0 KB
- 参考文档references/domains/tech-articles.md2.9 KB
- 参考文档references/domains/tickets.md1.7 KB
- 参考文档references/model-fingerprints.md4.1 KB
- 参考文档references/narrative-pass.md11.1 KB
- 参考文档references/professional-pass.md5.3 KB
- 参考文档references/rubric.md7.7 KB
- 参考文档references/style-pass.md6.7 KB
03 SKILL.md 正文
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.mdlast section) before flagging: clean grammar, formal tone in formal venues, and conventional templates are not evidence of AI.
04 同仓库更多 Skills
create-master
基于佛教经典文献,生成特定高僧大德的 AI 教学角色
writing-dna-skill
从至少 20 篇完整文章中蒸馏可复用的写作 DNA,分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格,并生成 Writing-DNA.md;按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.
stop-slop
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
shuorenhua
检查和清理中英文文本里的 AI 套路,适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求;按场景控制力度,同时保留事实、术语、语域和责任主体。
naming
Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.
humanizer-zh
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本,使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式:夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。