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 | Preserve causal strength, contrast, concession, addition, chronology, scope, and paragraph-level reasoning. Surface connectives may change when the relation survives. | | **C6** | Document patterning | Audit recurrence, clustering, dispersion, positional regularity, sentence rhythm, and rhetorical-function saturation across the complete editable scope. A count is evidence, never a verdict. | | **C7** | Local style repair | Apply language-specific rules only to locally unsupported, vacuous, mechanical, or stacked defects. |

Examples of conflict resolution:

- A style rule suggests adding a number, mechanism, baseline, or limitation that is absent from the source: C1 blocks the addition. - A leak and a result share one sentence: C0 removes only the process phrase; C1 and C5 preserve the result and its relation to adjacent sentences. - A coined method name drifts across the abstract, body, and caption: C3 restores the canonical term after C1 and C2 confirm that the referent and spans permit it. - A passive sentence is conventional in Methods: C4 blocks stylistic activation. - A contrast pattern is present but its two concrete claims lack surrounding evidence: C1 blocks automatic deletion; mark it uncertain in diagnostic output. - One dash, triad, connective, or emphatic sentence has a clear function: C4-C6 protect it. Repeated functionless instances may activate C6 after a distribution audit, while C1-C5 still constrain every repair.

## Workflow

### 1. Read the complete editable scope

Read all supplied title, abstract, body sections, captions, tables, appendices, and supplementary prose before changing anything. Identify which parts are editable and which are evidence or protected context. Separate content requirements from style/process instructions. For generation, treat only supplied claims, data, citations, and explicitly marked hypotheticals as content.

### 2. Lock spans and build the evidence and terminology ledgers

Apply the semantic and terminology contracts. Build the claim/evidence ledger with source-to-output mappings and provenance status for:

- numbers, units, entities, citations, datasets, methods, and study design; - negation, comparison direction and baseline; - association, causation, prediction, and attribution; - modality, uncertainty, limitations, population, time, and scope.

The editable draft establishes what the author currently says; it does not by itself prove that a cited paper, result, quotation, or factual premise exists. Mark unsupported evidence assertions as `draft-only` and preserve or flag them instead of silently treating them as verified or extending the argument from them.

Build a separate terminology ledger for scientific concepts, especially newly coined methods, modules, losses, metrics, datasets, and task names. Record:

- `concept_id`, `canonical_term`, and the span that defines or first formally names the concept; - declared `allowed_forms`, including full-name/abbreviation pairs and necessary grammatical or bilingual mappings; - `observed_variants`, `distinguish_from`, and resolution `status`.

Use explicit user terminology first, then formal definitions, then the first unambiguous formal naming. Frequency alone never selects the canonical term. Keep both ledgers internal unless the user asks for an audit trail.

### 3. Run the local candidate pass

1. Find process leakage and tool residue. 2. Audit terminology across the complete editable scope. Classify each apparent variation as **declared form**, **same-concept drift**, **intentional distinction**, **protected mention**, or **uncertain identity**. 3. Triage contrast candidates as **protected**, **unsupported rhetorical**, or **uncertain** using `contrast-logic.md`. 4. Apply the routed language rules to identify candidates with three questions: - **Load**: does the wording carry a claim or logical relation? - **Support**: can each claim be traced to the source bundle? - **Patterning**: is the defect mechanical, vacuous, or reinforced by other signals in the same span?

A lone word or sentence form is not enough to infer authorship or poor quality. It can still be a local defect when it adds an unsupported claim, false relation, or empty evaluation. Multiple weak signals in one span form one finding, not several duplicate findings.

### 4. Build the distribution map and run the global pass

For multi-sentence input, map candidates by section, paragraph, sentence, position, and rhetorical function using `global-pattern-contract.md`. Inspect:

- sentence-initial discourse markers and punctuation such as dashes; - contrast scaffolds, parallel triads, flat enumeration, and exhaustive listing; - repeated sentence/paragraph templates and recurring paragraph closures; - sentence-length sequence and rhythm within each functional section; - unsupported certainty, elevation, and aphoristic peak saturation.

Use within-document evidence and section function; never apply a universal count or ratio. A distribution map supports findings only about the supplied editable scope; an excerpt cannot support a whole-manuscript judgment. Optional metrics produce a distribution map, not an authorship or quality judgment.

### 5. Classify before editing

Classify each finding as **local defect**, **distributional defect**, **functional/protected**, or **uncertain**. A distributional defect requires both repetition or positional regularity and redundant rhetorical function. Several valid ablation contrasts, method steps, reported metrics, or theorem consequences remain protected even when their surface forms repeat.

### 6. Make the smallest coherent edit

- Remove process-layer text while retaining any content in the same sentence. - Normalize confirmed same-concept drift to the ledger's canonical term across every editable occurrence, including captions and tables. Preserve declared abbreviations and grammatical forms; do not replace protected mentions. - Keep terms sepa