Academic Humanizer

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 for claim preservation, locked spans, deletion safety, and the internal claim ledger.
2. Terminology contract 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 for the local-to-document audit, distribution map, scope limits, and whole-document repair. Always load it for multi-sentence work.
4. Academic whitelist for protected scholarly forms in both languages.
5. Contrast logic 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 or Chinese rules.

Read worked examples on first use, after changing a rule, or whenever fact preservation, contrast, or over-correction is uncertain. Read metrics specification 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.

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.