dsh-humanizer
A writing skill for DeepSeek Harness (dsh). The core idea is not "write like a human", but "write like me": strip generic AI-sounding patterns on one side, learn your own writing fingerprint on the other, and turn any draft into something *you* wrote.
This is a skill for the agent, not an LLM wrapper: it performs no model calls. It only produces rules, fingerprints, scores, and rewrite briefs — the agent does the actual rewriting itself.
Install
# from GitHub (ships prebuilt lib/)
dsh plugin add github:lynote-ai/dsh-humanizer
# or, once published to npm:
# dsh plugin add dsh-humanizerOptional config (all fields have defaults):
- insert:
- id: dsh-humanizer
name: 'dsh-humanizer'
config:
strength: standard # light | standard | aggressive
storagePath: ~/.dsh/voice-profiles.json
maxExcerpts: 3Tools (8 — all deterministic, no model calls)
De-AI:
| Tool | Purpose |
|---|---|
humanize_scan | Detect AI patterns and return an AI-ness score plus the matched rules |
humanize_rules | Export the full rule catalogue (transparent, editable) |
humanize_rewrite | Return a rewrite brief (rules + issues found in this text); the agent applies it |
Personal voice clone:
| Tool | Purpose |
|---|---|
voice_import | Import samples → extract a style fingerprint → persist a profile |
voice_profile | Read one profile, or list all |
voice_remove | Delete a profile |
voice_score | Similarity between text and a profile (0–100 + per-feature breakdown) |
voice_rewrite | Return a rewrite brief (fingerprint + few-shot samples + issues found) |
How it works
src/core/ is a pure, dependency-free, unit-tested library:
rules.ts— AI-writing pattern catalogue (English + Chinese, modeled on stop-slop / Humanizer-zh)analyze.ts— de-AI scan: empty openers, clichés, hedging, template transitions, mechanical parallelism, summary endingsfingerprint.ts— style fingerprint: sentence length / burstiness, punctuation habits, stance (person, adverbs, contractions), preferred vocabulary, lexical richnessscore.ts— similarity scoring (0–100 + per-feature breakdown)render.ts— rewrite-brief construction
humanize_rewrite / voice_rewrite never call a model — they return a brief, and the agent rewrites in its own turn. voice_score's per-feature output is the hook for future "learn from feedback" iteration.
Example (agent's point of view)
User: I have a dozen tweets I wrote. Build me a "my voice" profile.
Agent:
1. voice_import(name="me-x", samples=[...]) → extract & store fingerprint
User: Rewrite this AI-written release post in my voice.
Agent:
1. voice_score(text=draft, name="me-x") → 41/100
2. voice_rewrite(text=draft, name="me-x") → get brief (fingerprint + samples + issues)
3. agent rewrites following the brief
4. voice_score(rewritten, name="me-x") → 83/100Development
npm install
npm run check # typecheck + build
npm test # unit tests (node --experimental-strip-types)
npm run build # tsc → lib/Design decisions
- No LLM calls — rules, fingerprints, and scores are deterministic and reproducible.
- Single self-contained bundle — no unpublished shared dependency, so
dsh plugin addjust works. - File-based persistence —
~/.dsh/*.json, no storage backend required.
License
中文说明
面向 DeepSeek Harness（dsh）的写作插件。核心理念是——不是「像人写」，而是「像我写」：一边去掉通用 AI 腔，一边学习你本人的写作指纹，把任意草稿改成「你写的」。
这是一个给 Agent 用的 skill，不调模型：只产出规则、指纹、打分和「改写 brief」，真正的改写由 Agent 自己完成。
- 去 AI 味：
humanize_scan（检测）/humanize_rules（规则库）/humanize_rewrite（改写 brief） - 文风克隆：
voice_import/voice_profile/voice_remove/voice_score（0–100 相似度）/voice_rewrite（改写 brief）
规则库中英双语覆盖（参考 stop-slop / Humanizer-zh）。指纹提取与打分均为确定性计算，可复现、可单测；Profile 默认持久化到 ~/.dsh/voice-profiles.json。
安装：dsh plugin add github:lynote-ai/dsh-humanizer
