/academic-humanizer

name: academic-humanizer
description: >-
  Draft, audit, or minimally revise English- or Chinese-language academic prose
  to reduce formulaic, vacuous, mechanically repetitive, or process-leaking
  language while preserving claims, evidence strength, logical relations,
  manuscript-wide terminology identity, document-level pattern variation, and
  scholarly register. Use for papers, abstracts, grants, cover letters, and
  reviewer responses when the user asks to de-AI, humanize, audit AI-like
  phrasing, or rewrite text without changing meaning. English is primary;
  Chinese is supported. Not for detector evasion, policy circumvention, pure
  translation, non-academic copy, or adding facts, citations, examples, or
  author experiences that the source does not contain.

Academic Humanizer

Improve academic prose by removing observable writing defects, not by imitating imperfection or optimizing an authorship detector. Preserve the author's facts, argument, uncertainty, and disciplinary voice. This skill does not guarantee how any reader or detector will classify a text.

Portability

This skill is agent-agnostic. Its core behavior is defined by `SKILL.md` and `references/`; Python is optional and supports reproducible diagnostics.

• Resolve `<skill-dir>` from the directory containing this `SKILL.md`.
• Let `<python>` mean an available Python 3 launcher, such as `python3`, `py -3`,

or `python`.

• Let `<input-file>` mean a user-authorized local text file. Quote paths that

contain spaces and use the host shell's path separator.

• Do not assume a fixed skill root, home directory, shell, operating system,

agent name, or path separator.

• `agents/openai.yaml` is optional interface metadata. Core behavior does not

depend on a particular agent runtime.

• If Python is unavailable, skip the scripts and apply the same contracts

directly.

Load the operating references

Read these before drafting or editing:

1. [Semantic contract](references/semantic-contract.md) for claim preservation, locked spans, deletion safety, and the internal claim ledger. 2. [Terminology contract](references/terminology-contract.md) for canonical terms, declared aliases, coined names, intentional distinctions, and the internal terminology ledger. Always load it for multi-span or manuscript-level work. 3. [Global pattern contract](references/global-pattern-contract.md) for the local-to-document audit, distribution map, scope limits, and whole-document repair. Always load it for multi-sentence work. 4. [Academic whitelist](references/whitelist-academic.md) for protected scholarly forms in both languages. 5. [Contrast logic](references/contrast-logic.md) for false-opposition triage in English and Chinese. Always load it; this is a cross-language semantic rule. 6. Route once by the majority language of editable prose, then read exactly one: [English rules](references/rules-en.md) or [Chinese rules](references/rules-zh.md).

Read [worked examples](references/examples.md) on first use, after changing a rule, or whenever fact preservation, contrast, or over-correction is uncertain. Read [metrics specification](references/metrics-spec.md) before running `scripts/metrics.py`; its output is descriptive evidence only.

Supported operations

• **generate**: draft from user-supplied claims, outline, data, and sources.
• **detect**: identify high-confidence defects without rewriting.
• **rewrite**: minimally revise supplied prose; this is the default when the user

asks to de-AI or humanize text.

• **edit**: apply the same minimal revisions to a named file.

Do not create another routing tree for paper section or discipline. Methods, Results, Discussion, reviewer responses, and grants use the same contracts; the whitelist handles legitimate register differences. Ask one direct question only when the requested genre changes what counts as acceptable and context does not resolve it.

Language route

Route on editable prose, excluding fenced code, formulas, block quotations, and a trailing reference list. Use orthographic tokens: each CJK character is one token and each contiguous Latin word is one token. This keeps embedded terms such as `Transformer` or `ImageNet` from outweighing the Chinese sentence around them:

`r = CJK tokens / (CJK tokens + Latin word tokens)`

• `r >= 0.5`: Chinese branch.
• `r < 0.5`: English branch.
• No countable prose: stop and ask for text or an intended output language.

English terms in Chinese prose and Chinese terms in English prose remain verbatim. If Python is available and the route is genuinely unclear, optionally run `<python> "<skill-dir>/scripts/metrics.py" "<input-file>" --route`. Routing is internal and never appears in the clean artifact.

Single arbitration order

Earlier rows win. References may elaborate this table but must not define a second priority order.

| Priority | Constraint | Operational meaning | |---|---|---| | **C0** | Artifact boundary | Process instructions, editor narration, and tool residue never enter the artifact. C0 applies only to process-layer text; it never authorizes deletion of real content. | | **C1** | Semantic fidelity | Every output claim maps to the source bundle; every material source claim remains represented. No added facts, relations, examples, citations, motivations, or limitations. | | **C2** | Locked-span protection | Quotations, formulas, code, references, citation keys, statistical notation, proper nouns, and requested verbatim text remain unchanged. | | **C3** | Terminology identity | One scientific concept uses one canonical term across the editable manuscript. Preserve declared full-name/abbreviation pairs, necessary grammatical forms, and intentional distinctions; never infer identity from similarity alone. | | **C4** | Academic register | Preserve functional hedging, passive voice, nominalization, discourse markers, and Chinese scholarly morphology. | | **C5** | Argument structure | Pre